On Group Popularity Prediction in Event-Based …eeweb.poly.edu/faculty/yongliu/docs/Meetup.pdfMeetup is an event-based social network in which users can form and join different interest

On Group Popularity Prediction in Event-Based Social Networks

ABSTRACTEvent-based social networks have recently emerged as an importantcomplement to online social networks. They enjoy the advantagesof both online social networks and offline social communities: of-fline social events can be conveniently organized online, and usersinteract with each other face-to-face in the organized offline events.Although previous work has shown that member and structuralfeatures are important to the future popularity of the groups, it isnot yet clear how different member roles and the interplay betweenthem contribute to group popularity. In this paper, we study a real-world dataset from Meetup — a popular event-based social networkplatform — and propose a deep neural network based method topredict the popularity of new Meetup groups. Our method usesgroup-level features specific to event-based social networks, suchas time and location of events in a group, as well as structural fea-tures internal to a group, such as the estimated member roles in agroup and social substructures among members. Empirically, ourapproach reduces the RMSE of the popularity (measured in RSVPs)of a group’s future events by up to 12%, against the state-of-the-artbaselines. Moreover, through case studies, our method also iden-tifies member and structure patterns that are most predictive ofa group’s future popularity. Our study provides new understand-ing about what makes a group successful in event-based socialnetworks.

CCS CONCEPTS• Information systems→ Social networking sites;

KEYWORDSEvent-based Social Networks, Group Popularity Prediction, CircularFingerprints, Role DiscoveryACM Reference Format:. 2018. On Group Popularity Prediction in Event-Based Social Networks . InProceedings of The International World Wide Web Conference, Lyon, France,April. 2018 (WWW’18), 10 pages.https://doi.org/10.475/123_4

1 INTRODUCTIONAs online social networks become more prevalent, people’s face-to-face interactions are reshaped by these networks. In this workwe focus on event-based social network (EBSN), an online socialnetwork whose members hold in-person events. Meetup [14] isone such online platform that allows members to find and joinonline interest groups, and organize face-to-face events in different

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).WWW’18, April. 2018, Lyon, France© 2018 Copyright held by the owner/author(s).ACM ISBN 123-4567-24-567/08/06.https://doi.org/10.475/123_4

categories, such as politics, books, games, movies, health, pets,careers, and hobbies, etc. While it is relatively easy to establishnew groups in EBSN, it takes much more effort from the grouporganizers and members to make a group popular and sustainable.It is therefore important to understand the key factors contributingto the popularity and sustainability of groups in EBSN, especiallynewly established groups. Insights obtained from such a study canbe used to guide the promotion, recommendation and investmenton EBSN groups by EBSN platforms and investors.

In this paper, we study the problem of group popularity predictionin EBSN, with a special focus on new groups.More specifically, wefocus on predicting the popularity (measured in number of RSVPs)of newly established interest groups in Meetup [14]. The mainquestions that we want to answer are: 1). can we predict the futuresuccess of new groups? 2). what are the observable factors that bestpredict a group’s success? Different from the previous studies ongroup popularity in the traditional online social networks, our studytakes into account the unique features of EBSN in the followingkey aspects:• Get-out-of-the-couch Effort: To participate in an offline socialevent, a user must be physically present at some specific venue ata scheduled time. Clearly, this takes more effort and commitmentthan participating in a pure online event. As a result, a user’sattention (the ability to be active in multiple groups) is a severelylimited resource, which must be accounted for in predicting thesuccesses of competing groups.

• Face-to-face Social Interactions with Implicit Social Relationships:Once users meet in person, they may form stronger bounds thanonline interactions. Thus, to predict a group’s success one mustaccount for stronger and more sustainable social ties than onlinecommunities. However, EBSNs normally don’t have the explicitsocial relations between group members, that are readily avail-able in online social networks, such as “friends-with” (Facebook)and “follower" (Twitter).

Contributions. We develop a novel approach to predict the popu-larity of newly formed groups in EBSN, achieving the state-of-the-artaccuracy. Our approach considers various factors: i) the group-levelfeatures, such as the past popularity of the group and the number ofevents, ii) the event-based features, such as location and schedule ofevents, iii) user-level features related to user’s attention: how activeis a user in an individual group, and how does the user distributeher activity/attention among multiple groups. Based on these fea-tures, the first key idea of our approach is the use of role discoveryto determine the importance of users in a group and the roles theyplay. Similar to any social community in real life, a group in EBSN ismore than a simple collection of members. A group’s characteristicsare mostly determined by the interaction among group members,and its success ultimately hinges on the “chemistry” among its mem-bers. Coincidentally, the second key aspect of our approach is theextension of circular and neural fingerprints techniques developedin chemistry [4, 22] to study how social ties between different typesof users contribute to group success. Specifically, armed with each

https://doi.org/10.475/123_4https://doi.org/10.475/123_4

WWW’18, April. 2018, Lyon, France

group member’s role, and event co-participation graphs generatedfrom those members’ activities, we combine the members’ roleswith these activity networks to predict a group’s success, extendingthe fingerprints techniques developed to correlate the characteris-tics of atoms along with the neighboring bonds to other atoms todetermine a molecule’s function. Our extension also accounts for auser’s limited attention by incorporating “attention-based" features,such as how many groups a user joined and how much time theuser spent in each group into the member-level feature set.

By applying our novel approach to the Meetup dataset, we obtaininteresting findings w.r.t. features that predict a group’s success:(1) The user roles we discover in a group’s social network are goodpredictors of the group’s popularity, more than any other member-level features. (2) The most relevant user roles contributing to agroup’s popularity are not “organizer-like members", but “ordinarymembers" who have similar activity levels with their friends. (3) Themost important substructures (interaction patterns) are not combin-ing all the most important roles, but follow different combinationpatterns for different types of groups.

Outline. The rest of this paper is organized as follows. Section2 introduces the related work on group behavior prediction insocial networks, role discovery techniques and structural featureextraction. Section 3 describes the method we propose to solve theproblem of predicting the groups’ future popularity. In Section 4,we evaluate the performance of the proposed method using theMeetup dataset. Finally we conclude our work in Section 5.

2 RELATEDWORKEvent-based Social Networks. Event-based social networks werefirst studied by Liu et al. [11]. Topic, location, and time preferencesof individual users have been used to make event recommendationsto users [1, 2, 8, 13, 18, 25, 26, 28]. In a related work, Liu et al. [12]and Pramanik et al. [17] have investigated group-level factors thatcontribute to a group’s success or failure. Our work also focuseson predicting group popularity. We not only use a very different(and more effective) methodology, but also expand the feature set toinclude unique group-level features in EBSN, such as event venueand schedule, and combine member roles with the structure ofsocial network.

Group Popularity Prediction. There are two major lines ofresearch for this problem. One focuses on characterizing the evo-lution of online social network popularity by applying mean-fieldepidemic models to the time series of the “daily active user", withoutuser-level or network structural information [20, 27]. The otherfocuses on using general group features to make predictions [12,17, 19]. In contrast, our approach uses richer information and con-volutes member roles, member’s attention-capacity features, withtheir activity network structure to improve the prediction accuracy.

Role Discovery. The goal of network role discovery is to clas-sify network nodes according to the roles they play in the net-work [5, 23, 24, 29]. Given a graph along with node features, theprocess of role discovery relies on defining node equivalence. Vari-ous types of equivalences have been introduced in previous studies,such as graph-based equivalences, feature-based equivalences, andhybrid equivalences [23]. To capture both structural and featureequivalences between members, the information that we use for

role discovery represents both members’ intrinsic behaviors, suchas the numbers of events/groups that they have participated in, aswell as the partial structural behaviors of members by includingtheir one-hop-neighbors’ features (see Table 1).

Structural Feature Extraction. Several studies on group pop-ularity prediction have found that a group’s main characteristicscan be largely related to the interaction patterns among their mem-bers [12, 17, 19]. These patterns can be represented by the nodeattributes and link structures of a graph generated based on mem-bers’ interactions. While multiple methods, such as “deepwalk” [16]and “node2vec” [6] have been proposed for mapping structureddata to real-valued feature vectors, these mainly focus on trans-forming graphs into node features, instead of obtaining featuresfor the graphs. Some recent studies [3, 9, 15, 21, 30] also focus ontransforming substructures or subgraphs in large graphs to featurevectors, however the resulting latent vectors cannot be easily inter-preted to obtain insights about group popularity. In contrast, weextend the circular and neural fingerprints techniques in chemistryinformatics to gain important understanding on how subgraphsamong different types of members contribute to group success.

3 PROPOSED METHODIn this section, we first give a metric of a group’s popularity anddefine our prediction problem in the context of the Meetup EBSN.We then propose our prediction method, leveraging on the group-level features (Section 3.2) and member-level features (Section 3.3).Finally, Section 3.4 presents our overall method combining thesegroup-level and member-level features to make predictions.

3.1 Meetup Group Popularity PredictionMeetup is an event-based social network in which users can formand join different interest groups online, and organize and par-ticipate in face-to-face social events offline. The group organizerscreate events, and each event has specified time, location and topic.The information about new events will be sent to group membersthrough emails or website notifications. Each group member de-cides whether she will participate in the new events based on hertime, location, and topic preferences, and then responds by sendingRSVPs (“yes", “no", or “maybe"). Figure 1 illustrates the main com-ponents in Meetup social network. With the definitions of groups,events and users in Meetup, the group popularity prediction prob-lem can be defined as:

Definition 3.1. Given all the related information of a groupwithina time window of [0,n] months, and a time interval ofm months,predict the group’s total RSVP number (popularity) within a futuretime window of [n +m, 2n +m] months.

The time interval ofm months can be chosen to eliminate theeffect of seasonal event holding patterns. For example, a skiinggroup may have high activity level only in winter (October ∼ De-cember) while the RSVP numbers in summer could be small. Usingwinter RSVP numbers to predict summer RSVP numbers, or viceversa, is not a meaningful prediction task. In our study, we calculateprediction accuracy for multiple choices ofm, then take the averageto represent the overall accuracy.

On Group Popularity Prediction in Event-Based Social Networks WWW’18, April. 2018, Lyon, France

5

1

2

3

4

0

7

8

9

6

Organizer

Events

Users

OrganizerEvents

Users

Transform to Social Graph

Virtual Link

0

1

2

34

5

6

8

9

Group 1

Group 2

Figure 1: Components in Meetup Social Network. The left-hand side shows components of two groups: every solid linefrom a user to an event represents a “yes" RSVP; When twousers participate in the same events, a “virtual link" betweenthem is created to represent their potential interaction; User“5" is the “social spanner" who joins two groups and user “7"is an inactive user who hasn’t participated in any event; Theright-hand side: using virtual links and removing events weget a homogeneous graph in which all nodes are users.

3.2 Group-level FeaturesThe most straight-forward features to use for popularity predictionare the summary statistics of each group. So we start with extract-ing “group-level features", which are various summary statistics ofa group without examining the detailed features of each member inthe group. We list the descriptions of fourteen group-level featuresfor each Meetup group in Table 1, such as the scheduled time dis-tributions of its events, the location distributions over its venues,and RSVP counts of all members, etc.

3.3 Internal Group FeaturesAlthough by only using group-level features we can achieve goodperformance, we intend to improve the accuracy further by inves-tigating the internal group features. Internal features of a groupcan be defined as all the features that are related to each individualmember in the group. These features should include the first-orderfeatures that can be directly calculated using basic statistics, such asthe past attendance of a member and how many groups a memberhas joined. They should also include the second-order features thatrequire further processing, such as member’s role discovery andstructural feature extraction.

3.3.1 Member-level Feature Extraction. We start with construct-ing social graph for each group from which the features are ex-tracted. For each given group д, the social graph is defined as:

Definition 3.2. Gд = (U д ,Eд ,W д), where U д is the memberset in group д, Eд denotes all edges between members andW дrepresents all edge weights. Two members ui and uj are defined to

be connected if they co-participated in at least one event in groupд. The weight on each edge is calculated as the number of eventstwo users have co-participated in (Figure 1).

Following [7], we propose twelve member-level features listedin Table 1: Feature m1∼m6 represent “who you are", i.e., featuresrelated to the member’s own characteristics, and feature m7∼m12represent “who you know", i.e., features related to her neighbors’characteristics.

3.3.2 Member Role Discovery. To find role features of eachgroup member, following [7], we use Non-negative Matrix Factor-ization (NMF) [10] for our role assignments. For a given a member-feature matrix X ∈ Rn×f , we generate a rank-r approximation(r < min(n, f )) MF ≈ X where each row of M ∈ Rn×r representsa node's membership in each role and each column of F ∈ Rr×frepresents how membership of a specific role contributes to theestimated feature values. With a distance measure ∥ · ∥, the problemcan be simplified as:

minF∈Rr×f ,M∈Rn×r

∥MF-X∥

subject to M, F ≥ 0. In practice, considering the feature numberf = 12, we choose the role number r = 6.

Taking the member-role matrix Mg for group д generated by therole discovery method, we sum over all the members (rows) andget the group’s role distribution vector

{Vgj =

∑ni=1 M

gij, 1 ≤ j ≤ r

},

then we stack all the vectors {Vg} to form a group-role matrixΩ ∈ Rp×r where p represents the number of groups and r is thenumber of roles. Thus the correlation between role X and grouppopularity can be calculated as the correlation between the columncorresponding to role X in Ω and all groups’ RSVP numbers.

In Figure 2 (top) we calculate the Pearson correlation betweenthe member feature vectors and their group’s popularity. The weakcorrelations (in the range of [−0.2,+0.2]) indicate that no individualmember feature plays an important role in the popularity of group.

Figure 2 (bottom) shows that role discovery (i.e. combining mul-tiple features to form a role) significantly increases the correlation,where roles are now positively strongly correlated with group popu-larity (in the range of [+0.45,+0.75]). Each role is a linear combina-tion of multiple raw member features. Thus by analyzing the roles,we can get a better understanding of how certain combination offeatures contributes to the group’s popularity, and at the same time,creates a role profile for each member.

3.3.3 Structural Features. As observed in the previous studies,structural features of a group play important roles in affectingthe group’s characteristics. However, the existing work does notconsider the roles of members within the group when detectingstructural patterns, while intuitively the members’ roles should betaken into account. Let’s consider an example group, “NY Tech",the largest group in the Meetup social network. Every week theorganizers of “NY Tech" create a technical conference-like eventand sometimes invite an expert on some topic to be the speaker(Figure 3). In this case, without differentiating between the tworoles, “organizer" and “speaker", there is no difference betweenthe two social network graphs. However, with the knowledge oftheir roles, one may easily notice that the subgraph surrounding


Group

-levelfeatures

g1. Entropy of the time distribution over all timesthe events are heldg2. Average distance between any two events thegroup heldg3. Variance of the “event-event" distancesg4. Average distance between any event and anyparticipating membersg5. Variance of the “event-member" distancesg6. Average distance between any member andany other member in the same groupg7. Variance of the “event-member" distancesg8. Entropy of the location distribution over allvenues the event are heldg9. Density of the group’s social graphg10. Total degree of the group’s social graphg11. Event number the group has heldg12. Average RSVP number of all past eventsg13. Variance of RSVP numbers of past eventsg14. Sum RSVP number of past events.

Mem

ber-levelfeatures

m1. Total degree of the memberm2. Event number the member has participated in current groupm3. Group number the member has joinedm4. Entropy of member’s attendance distribution over the groupsthe member joinedm5. Entropy of event number distribution over the groups themember joinedm6. Entropy of event fraction distribution over the groups themember joinedm7. Average degree of the member’s 1-hop neighborsm8. Average event number the “1-hop neighbors" have participatedm9. Average group number the “1-hop neighbors" have joinedm10. Average entropy of “1-hop neighbors’" attendance distribu-tion over the groups they have joinedm11. Average entropy of “1-hop neighbors’" event number distri-bution over the groups they joinedm12. Average entropy of “1-hop neighbors’" event fraction distri-bution over the groups they joined

Table 1: Group-level and Member-level Features

m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12Feature Index

−0.3

−0.2

−0.1

0.0

0.1

0.2

0.3

Correlation

Raw Feature Correlation with Group's RSVP number

Role A Role B Role C Role D Role E Role FRole Index

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Correlation

Role Correlation with Group's RSVP number

Figure 2: Feature Correlation with Popularity. Role discov-ery method combines multiple raw member features intoone distribution to represent a member’s profile.

the organizers (in the upper right) is more stable than the onesurrounding the speaker (in the lower right) since the “speaker"is very likely to be changed in the next event, while the group’sorganizer remains the same.Circular Fingerprints. In order to extract structural features em-bedded with nodes’ roles, we use the “circular fingerprints" al-gorithm [22]. Circular fingerprints is a popular tool for handlinggraph-structured data in chemistry. It was first designed for molec-ular characterization, similarity searching, and structure-activitymodeling. A molecule consists of atoms with different types. How

Ordinary users

Speaker

Organizer

Stable

Unstable

Figure 3: A subgraph in the “NY Tech Meetup" Group

different types of atoms are bounded together is the key factorthat determines the characteristics of the molecule. In the contextof event-based social network, we draw the analogy between amolecule and a group. We assume the members of a group areanalogous to the atoms of a molecule, and the social ties betweenmembers are analogous to the chemical bonds (Figure 4). Then wecan study how the subgraphs between members contribute to thegroup popularity using the circular fingerprints framework. Thefingerprint generation process is accomplished mainly in two steps:(1) The algorithm starts with assigning an initial identifier to each

atom (member) in the molecule (group). This identifier cap-tures some basic information of the atom (member) such asatomic number, connection count, etc. In our case the “basicinformation" is the role distribution attached to each member.

(2) After that, a number of iterations are performed to combinethe initial atom (member) identifiers with identifiers of neigh-boring atoms (members) until a specified radius (number ofhops from this atom) is reached. For example, in iteration 1,the identifiers of all "one-hop neighbors" of the target atomare combined with the identifier of the target atom to generate


the new identifier. Each iteration captures larger and largercircular neighborhoods around each atom (member), which arethen encoded into single integer values using a suitable hashingmethod, and these identifiers are collected into a list. In thisway, each subgraph is generated by a member along with herneighbors within a certain radius.

The identifier list (also called “fingerprints") is then used to char-acterize the properties of the molecule. In our case, we use thefingerprints as structural features to predict the group’s popularity.

Radius 0:

Radius 1:

Radius 2:

….

Identifier0 Identifier1 Identifier2 Identifier3 ……..Circular Fingerprints:

….

Figure 4: Subgraphs Detected by Circular Fingerprints in So-cial Network. Circular Fingerprint scans the network for allsubgraphs under certain radius. Each subgraph is then en-coded into an integer identifier. Integer identifiers of all sub-graphs constitute the circular fingerprints.

Group-RoleNeural Fingerprints.Although circular fingerprintsis a convenient tool to study social graph, it has several limitations:1) the algorithm can only handle graphs with fixed sizes; 2) even ifthe graphs vary a little bit, the resulting fingerprints can be quitedifferent, making the features highly vulnerable to noise. Overcom-ing these limitations, Duvenaud et al. [4] proposed a convolutionalneural network, where each neural network layer simulates theupdating operation in circular fingerprints (Figure 5).

We now extend the convolutional fingerprinting algorithm tosolve our problem. We denote this approach as the Group-Role Neu-ral Fingerprints: For radius 0, the first hidden layer in the networktakes the initial member-role matrix which is produced by the pre-vious role discovery step as the input, then the output of this layergoes in two directions: in one direction the output is directly calcu-lated as the radius 0 fingerprint; in the other direction, the outputis updated with the adjacency matrix through a “feature update"operation. In this update operation, the member-role matrix is up-dated so that each member’s 1-hop neighbors’ role distributionvectors are added to the corresponding row of member-role matrix.In this way, the algorithm iterates until a certain radius is reached.After each iteration, more and more local structural informationare captured. The detailed operations are presented in Algorithm 1.As the result, the final fingerprints are calculated as the summationof the fingerprints at each radius and then taken as input of linearregression algorithm to produce prediction of the RSVP number(popularity). The number of hidden layers equals to the given ra-dius R. The neural network’s weights H0, ...HR andW0, ...WR are

𝒓(𝒂)

𝒇𝟏

𝒗(𝒂)

Feature Update: 𝒓(𝒂) + 𝒓(𝒏𝒆𝒊𝒈𝒉𝒃𝒐𝒓𝒔(𝒂)) Iterative Step

…….

adjacency matrix

∙ 𝑯𝟎 ∙ 𝑯𝟏

𝒇𝟐

𝒇𝟎∙ 𝑯𝟎, 𝝈(∙ 𝑾𝟎), 𝒔𝒐𝒇𝒕𝒎𝒂𝒙(∙)

∙ 𝑯𝟏, 𝝈(∙ 𝑾𝟏), 𝒔𝒐𝒇𝒕𝒎𝒂𝒙(∙)

𝒇 = 𝒇𝟎+𝒇𝟏+𝒇𝟐+⋯

∙ 𝑯𝟐, 𝝈(∙ 𝑾𝟐), 𝒔𝒐𝒇𝒕𝒎𝒂𝒙(∙)

adjacency matrix

Feature Update

Figure 5: Group-Role Neural Fingerprints Algorithm. “Fea-ture Update" operation: each member’s feature is updatedso that the 1-hop neighbors’ features and neighboring edgefeatures are added to the initial member feature.

learned from the training process. The σ and softmax functions aregiven as:

σ (x) = 11 + e−x

, softmax(z) = ezj∑K

k=1 ezk

for j=1...K.

Since it is a convolutional network-like structure, the number ofhidden units at each hidden layer and the length of fingerprints fare bothm. For experiments, we choosem to be 10 and the size ofinput layer to be 6 (equals to the length of role distribution vector),but the results are robust to these choices of values.

Algorithm 1: Group-Role Neural FingerprintsInput :group’s social graph, members-role matrix, radius

R, hidden layer weights H0, ...HR, output layerweightsW0, ...WR , length of role distributionvector t , fingerprint lengthm.

Initialize :fingerprint vector f1×m ⇐ 0s1 for each member a in social graph do2 r (a)1×t ⇐ a’s role distribution vector3 end4 for L = 0 to R do5 for each member a in social graph do6 r (1)...r (N ) = role distribution vectors of neighbors(a)7 v1×t ⇐ r (a)1×t +

∑Ni=1 r (i)1×t

8 r (a)1×m ⇐ σ (v1×t · HLt×m)9 f L1×m ⇐ softmax(r (a)1×mWL)

10 f1×m ⇐ f1×m + f L1×m11 end12 end

Return : real-valued vector f

3.4 Combining Group-level and InternalFeatures

As illustrated in the left half of our deep neural network in Figure 6,we have two types of features, namely, the group-level features


Results

….

….

….…

.

….

….…

.

Member-level Features

+

Group’s Social Graph

Group-level Features

Neural Fingerprints Network

Multilayer Perceptron

Multilayer Perceptron

Neural Fingerprints

Figure 6: Combining Group-level Features and InternalGroup Features

and our convolution of the member-level features with the group’ssocial graph (Group-Role Neural Fingerprints). We now proceedto explore different ways to combine the group-level features andinternal features to make better prediction than only using onechannel of them. We investigate three combination methods:

• Method 1: Combine independent group-level and member-levelpredictions. Predict group popularity using group-level andmember-level features independently, then combine the predictions asα×(group-level prediction)+(1-α)×(member-level prediction), whereα ∈ [0, 1]. The value of α is chosen over validation data.

• Method 2: Clustered adjusted combination. We first cluster thegroups based on the group-level features and find the optimal αweight for each cluster. Whenever a newly formed group arrives,we calculate the inverse of the distances from the new group tothe existing group clusters to find the optimal weight for the newsample group. The optimal α∗ is calculated as:

α∗ =

∑Ni=1 αi/di∑Ni=1 1/di

. (1)

where di is calculated as the average distance between the newgroup and all groups in cluster i .

• Method 3: Deep neural network based combination. As illustratedin Figure 6, a deep neural network is used to combine group-leveland member-level features. It is a combination of our neuralfingerprints network with two Multilayer Perceptrons (MLPs). Itis the best method in our experiments.

4 PERFORMANCE EVALUATIONTo evaluate the performance of our proposed method, we conductexperiments on the Meetup dataset. In section 4.1 we provide abrief description about our dataset. Then we test the predictionpower of group-level features, member-level features, and group-role neural fingerprints respectively in Section 4.2. In Section 4.3,we compare the accuracy of the three methods of combining group-level features and member-level features with three competitivebaselines. Finally in Section 4.4 and 4.5, we analyze the importanceof various types of member roles along with the interaction patterns(social structures) between the roles for predicting group success.

4.1 Dataset DescriptionWe crawled all Meetup groups located within 50 miles of New YorkCity (NYC), from March 2003 to February 2015, including all therelated meta-data. Table 2 summarizes the salient statistics of thecollected dataset.

Name Value

Number of groups 17,234Number of users 1,101,336Number of events 1,025,719Number of RSVPs 8,338,382Number of venues 93,643

Avg. Members per group 274.13Avg. Groups a user joins 3.54Avg. Events per group 72.26

Avg. Participants per event 5.67Avg. Events per active user 9.38

Table 2: Dataset Statistics

4.2 Group Popularity PredictionIn this section, we show how member-level and group-level fea-tures can be used to predict the popularity of groups at differentprediction intervals. Unlike the experiment settings introducedby [12], [17] and [19], which include all groups of different sizesand ages, we only focus on predicting future RSVP numbers of newgroups in each year. In our experiments, features are extracted fromthe first three months starting from the time a newly formed groupheld its first event. We then make prediction of the RSVP numberwithin another time window of three months in the future after atime interval ranging from one month to ten months. The predictedRSVP numbers are tested against the true RSVP numbers. We usethe Root Mean Squared Error (RMSE) to measure RSVP predictionaccuracy:

RMSE =

√√1n

n∑i=1

(yi − ŷi )2,

where yi is a target group’s actual RSVP number and ŷi is thepredicted RSVP number. Sincewe only focus on predicting the RSVPnumber of newly created groups, the RMSE is not dominated bysingle very large group in our dataset. In datasets where a few largegroups dominated the RMSE, we recommend using the normalizedRMSE as a metric of accuracy. After filtering out the groups withoutvalid information to calculate the features, we have more than 7,000new groups alongwith their features and RSVP numbers.We choosethe first 80% groups along the time line as training set and the restto be the testing set.

4.2.1 Group-role Neural Fingerprints vs. Raw Member Features.To demonstrate the Group-role Neural Fingerprints (GRNF) cantruly improve the prediction accuracy and avoid the influence ofgroup-level features, we first conduct group popularity predictionwith raw member features using the classic machine learning meth-ods (Linear Regression, Support Vector Regression, Multilayer Per-ceptron, and Random Forest). We then input the raw member fea-tures with adjacency matrix of the social graph to our proposed


group-role neural fingerprints algorithm. The parameters for train-ing GRNF include: number of training epochs (50), batch size (100),learning rate (exp(-1)*3), activation function (relu), L1 regulariza-tion (exp(-4)), L2 regularization (exp(-4)), fingerprint length (10),and convolution layer sizes (10*10*10). Finally, the obtained GRNFare input to MLP to predict group popularity.

We can see from Table 3 that Group-role Neural Fingerprints cansignificantly reduce the prediction errors of raw member featurebased predictions at different prediction intervals (1 to 10 months).The performance improvement is statistically tested by the t-testscores presented in last two columns. The performance gain ofGRNF is due to its capability of taking advantage of the structuralinformation within a group.

4.2.2 Comparison with BaselineMethods. Most studies on grouppopularity prediction, such as [12], [19], and [20], directly use group-level features or aggregate member-level features into group-levelfeatures. We compare our method which uses both group-levelfeatures and internal features (shown in Table 1) with three com-petitive baselines:• Baseline 1 [12]: in addition to meta information about the groups,such as “number of group members", “number of events" and“group join mode" etc., it also uses the averaged member-level fea-tures, such as “average event attendance of members" and “stan-dard deviation of event attendance of members" etc. “Structuralfeatures" are introduced based on a bipartite graph generated byevents and users, without distinguishing the user types.

• Baseline 2 [19]: it demonstrates that structural features like triadscounts and clustering coefficients have strong predictive powerfor predicting the longevity of the group’s lifecycle in an onlinesocial messaging network.

• Baseline 3 [20]: it uses epidemic model of differential equationsto fit the evolution curve of group’s popularity. One advantageof this model is that it provides decent accuracy by only usingthe time series of daily active users (DAU). This simple baselineacts as a sanity-check to whether the time series of the group’spast popularity (the number of RSVPs) alone could determinethe group’s future popularity.

For baseline 1&2, we implement most of the original features and ap-ply four classical machine learning algorithms: Linear Regression,Support Vector Regression, Multilayer Perceptron, and RandomForest, and choose the one with the best performance as the rep-resentative for each baseline. For baseline 3, we use the DAU of agroup in the first three months to fit the curve and the rest time totest the accuracy.

The results in Table 4 show that our final proposed approach(Combination Method 3) clearly outperforms all baselines in allprediction horizons, ranging from predicting the average 3-monthRSVP numbers in the immediate next three months to predictingthis quantity ten months after the last record in the training data.As expected, for all methods, the error of predicting nearer future issmaller. Thus, it is important to contrast the accuracy gains of ourmethod against all baselines, which range from 3.91% to 12.32%. Thestatistical significance of the performance improvement is verifiedby the p-values reported in the last column of Table 4. We seesimilar results for different averaging windows (4 and 5 months).Due to space limit, we don’t include the results here.

4.2.3 Impact of Different Combination Methods. Since our pre-dictions are made through two independent feature sets, we cancombine features using different methods proposed in Section 3.4.In Table 5 we compare the accuracies of the three combinationmeth-ods. For method 2, we try two clustering methods: K-means andDBSCAN (choose the one with better performance). We can see thatMLP adjusted combination yields the best average RMSE=101.65for all prediction intervals, improving the best baseline (baseline 1)by 11.3%.

Combining Method Method 1 Method 2 Method 3

Average RMSE 108.74 108.49 101.65

Gain from Best Baseline (%) 5.41 5.42 11.3Table 5: Performance of Three Combination Methods

4.3 Member Role AnalysisIn this section, we analyze the results produced by role discoverymethod in Section 3.3.2 and try to answer the question: “who arethe members contributing the most to a group’s future popularity?"

As detailed in Section 3.3.2, we can get the role distributionvector for a group by aggregating the role distribution vectors ofall its members. We then calculate the Pearson correlation betweeneach group’s RSVP number and its role distribution. In addition,every member is assigned to a role according to the largest elementin her role distribution vector. By averaging the feature vectorsof all members assigned to each role, we obtain a representativefeature vector for each role to study user’s typical behavior.

Inspired by [7], we infer and interpret each role by analyzingthe representative feature vector for each role and comparing eachrole’s “own-feature part" and its “neighbor-feature part" in its rep-resentative feature vector, that is, we infer the role of a user byanalyzing “who she is” and “who she knows”. For example, if weobserve a user has many more connections with her friends (re-flected by the degree of the node representing the user in the socialgraph defined in Definition 3.2) than all her friends have, then weknow she is probably the most the active user in her local socialnetwork, and we can further infer that there is a good chance thatshe is an event/group organizer in reality. In our experiments, weset the role number to six and get the representative feature vectorsfor the six roles.

Role A (Group Organizers): The user potentially interactswith a large number of people (average own degree=3,568.24) and ismore active than her neighbors (average neighbor’s degree=473.14).Together with other information about Role A’s behaviors, such asjoining very few groups (average group number=1.12) and attend-ing as many events as possible (attended event fraction=0.73), weinfer that the user is potentially a successful event organizer. Wealso find that 33.6% of the users assigned to Role A have hosted(sent the first RSVP “yes") at least one event, which is higher thanthe percentages in other roles. The correlation between Role A andgroup RSVP number is 0.514.

Role B (Inactive Followers): Compared with other roles, theuser’s neighbors are much more active than the user herself (aver-age own degree=2.19, while average neighbors’ degree=5,550.03),


Table 3: RMSE of Raw Member Features vs. RMSE of Group-role Neural Fingerprints (GRNF)

Interval Raw Member Features GRNF Gain(%) |t-statistic| p-valueLR SVR MLP RF

0m 93.49 93.81 177.08 94.08 89.49 4.27 >1.14 1.17 1.15 1.07 1.17 1.26 1.69 1.33 1.24 1.11 1.13


Role A – Role F Role A – Role C Role F – Role F

Figure 7: Subgraphs Contributing theMost to Group Popularity. Three types of learned subgraphs (radius=1) are: role A-F, roleA-C, and role F-F. For each subgraph type, two example groups with high popularity (top-100 popular in Meetup) that containthis type of subgraph are shown.

clear “Role A - Role F", “Role A - Role C” or “Role F- Role F” pat-tern. On the other hand, when we examine unpopular groups, wecouldn’t find the previous three clear graph patterns.

Role A (Organizers) - Role F (Followers): Normally seen insocial-based groups such as popular soccer groups. There is anorganizer (role A) for each game, and several active players (role F).Role C (Conference Audience) seldom exists in such group.

Role A (Organizers) - Role C (Audience): Frequently seen intechnical seminars. A large number of role C (Audience) membersexist in the group with only one or two organizers. The intensityof social connections between members in this type of groups isweaker than what is common in more social-based groups.

Role F (Followers) - Role F (Followers): Normally seen indancing groups. Similar with role A-F structure, the social intensityin this type of groups tends to be strong. They differ in that theconnections among ordinary members are stronger.

Interestingly, we find that topics of the groups are highly corre-lated to the role distribution and structures of their social graphs.For example, soccer groups like “Fun Times Soccer NYC” and “Brook-lyn Pickup Soccer Group” tend to have a lot of “Role A-Role F"structures, technical seminars like “New SQL Database Meetup” and“New York Social Society” normally are full of “Role A-Role C" struc-tures, and dancing groups, such as “NYC Zumba! Dance and Fitness”and “The Salsa Latin&Ballroom Dance Group”, are likely to havemore “Role F-Role F" structures. On the other hand, we can see thatif a group organizer wants to gain more group popularity in thefuture, she probably needs to build up such interaction patterns inan early stage.

5 CONCLUSIONIn this paper, we proposed a deep neural network method to predictthe future popularity of groups in event-based social networks. Ourmethod outperformed all state-of-the-art methods. Along the way,we have analyzed a few key factors contributing to these predic-tions. Specifically, we showed that location and time are importantgroup-level features. We also demonstrated that member roles andinteraction among members with different roles, characterized byneural fingerprints, can better represent the intrinsic member be-haviors and the social structure of a group than the raw memberfeatures and the activity graph. Through case studies in Meetup,we showed that the most relevant user roles to a group’s popularityare not “organizer-like members", but “ordinary members". We alsofound that the most important substructures are not combining allthe most important roles, but follow different combination patternsfor different types of groups.


REFERENCES[1] Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations

in location-based social networks: a survey. GeoInformatica 19, 3 (2015), 525–565.[2] Hao Ding, Chenguang Yu, Guangyu Li, and Yong Liu. 2016. Event Participation

Recommendation in Event-Based Social Networks. In International Conferenceon Social Informatics. Springer, 361–375.

[3] Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec:Scalable representation learning for heterogeneous networks. In Proceedings ofthe 23rd ACM SIGKDD International Conference on Knowledge Discovery and DataMining. ACM, 135–144.

[4] David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell,Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. 2015. Convolutionalnetworks on graphs for learning molecular fingerprints. In Advances in neuralinformation processing systems. 2224–2232.

[5] Sean Gilpin, Tina Eliassi-Rad, and Ian Davidson. 2013. Guided learning for rolediscovery (glrd): framework, algorithms, and applications. In Proceedings of the19th ACM SIGKDD international conference on Knowledge discovery and datamining. ACM, 113–121.

[6] Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning fornetworks. In Proceedings of the 22nd ACM SIGKDD international conference onKnowledge discovery and data mining. ACM, 855–864.

[7] Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, SugatoBasu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li. 2012. Rolx:structural role extraction & mining in large graphs. In Proceedings of the 18thACM SIGKDD international conference on Knowledge discovery and data mining.ACM, 1231–1239.

[8] Houda Khrouf and Raphaël Troncy. 2013. Hybrid event recommendation us-ing linked data and user diversity. In Proceedings of the 7th ACM conference onRecommender systems. ACM, 185–192.

[9] Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graphconvolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[10] Daniel D Lee and H Sebastian Seung. 2001. Algorithms for non-negative matrixfactorization. In Advances in neural information processing systems. 556–562.

[11] Xingjie Liu, Qi He, Yuanyuan Tian, Wang-Chien Lee, John McPherson, andJiawei Han. 2012. Event-based social networks: linking the online and offlinesocial worlds. In Proceedings of the 18th ACM SIGKDD international conference onKnowledge discovery and data mining. ACM, 1032–1040.

[12] Xiang Liu and Torsten Suel. 2016. What makes a group fail: Modeling socialgroup behavior in event-based social networks. In Big Data (Big Data), 2016 IEEEInternational Conference on. IEEE, 951–956.

[13] Augusto Q Macedo, Leandro B Marinho, and Rodrygo LT Santos. 2015. Context-aware event recommendation in event-based social networks. In Proceedings ofthe 9th ACM Conference on Recommender Systems. ACM, 123–130.

[14] Meetup 2017. Meetup. (2017). Retrieved October 31th, 2017 from https://en.wikipedia.org/wiki/Meetup_(website)

[15] Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, andSanthoshkumar Saminathan. 2016. subgraph2vec: Learning distributed represen-tations of rooted sub-graphs from large graphs. arXiv preprint arXiv:1606.08928(2016).

[16] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learningof social representations. In Proceedings of the 20th ACM SIGKDD internationalconference on Knowledge discovery and data mining. ACM, 701–710.

[17] Soumajit Pramanik, Midhun Gundapuneni, Sayan Pathak, and Bivas Mitra. 2016.Can i foresee the success of my meetup group?. In Advances in Social NetworksAnalysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on.IEEE, 366–373.

[18] Zhi Qiao12, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo, and Yanchun Zhang.2014. Event recommendation in event-based social networks. (2014).

[19] Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, andJohn E Hopcroft. 2016. The lifecycle and cascade of wechat social messaginggroups. In Proceedings of the 25th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee, 311–320.

[20] Bruno Ribeiro. 2014. Modeling and predicting the growth and death ofmembership-based websites. In Proceedings of the 23rd international conferenceon World Wide Web. ACM, 653–664.

[21] Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. 2017. struc2vec:Learning node representations from structural identity. In Proceedings of the 23rdACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM, 385–394.

[22] David Rogers and Mathew Hahn. 2010. Extended-connectivity fingerprints.Journal of chemical information and modeling 50, 5 (2010), 742–754.

[23] Ryan A Rossi and Nesreen K Ahmed. 2015. Role discovery in networks. IEEETransactions on Knowledge and Data Engineering 27, 4 (2015), 1112–1131.

[24] Yiye Ruan and Srinivasan Parthasarathy. 2014. Simultaneous detection of commu-nities and roles from large networks. In Proceedings of the second ACM conferenceon Online social networks. ACM, 203–214.

[25] Zhenhua Wang, Ping He, Lidan Shou, Ke Chen, Sai Wu, and Gang Chen. 2015.Toward the new item problem: context-enhanced event recommendation inevent-based social networks. In European Conference on Information Retrieval.Springer, 333–338.

[26] Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013. Lcars: alocation-content-aware recommender system. In Proceedings of the 19th ACMSIGKDD international conference on Knowledge discovery and data mining. ACM,221–229.

[27] Tianyang Zhang, Peng Cui, Christos Faloutsos, Yunfei Lu, Hao Ye, Wenwu Zhu,and Shiqiang Yang. 2016. Come-and-go patterns of group evolution: A dynamicmodel. In Proceedings of the 22nd ACM SIGKDD International Conference onKnowledge Discovery and Data Mining. ACM, 1355–1364.

[28] Wei Zhang, Jianyong Wang, and Wei Feng. 2013. Combining latent factor modelwith location features for event-based group recommendation. In Proceedings ofthe 19th ACM SIGKDD international conference on Knowledge discovery and datamining. ACM, 910–918.

[29] Yao Zhang, Bijaya Adhikari, Steve TK Jan, and B Aditya Prakash. 2017. Meike:Influence-based communities in networks. In Proceedings of the 2017 SIAM Inter-national Conference on Data Mining. SIAM, 318–326.

[30] Vincent W Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang,and Erik Cambria. 2016. From node embedding to community embedding. arXivpreprint arXiv:1610.09950 (2016).

https://en.wikipedia.org/wiki/Meetup_(website)https://en.wikipedia.org/wiki/Meetup_(website)

Abstract1 Introduction2 Related Work3 Proposed Method3.1 Meetup Group Popularity Prediction3.2 Group-level Features3.3 Internal Group Features3.4 Combining Group-level and Internal Features

4 Performance Evaluation4.1 Dataset Description4.2 Group Popularity Prediction4.3 Member Role Analysis4.4 Social Structure Analysis

5 ConclusionReferences

Documents

On Group Popularity Prediction in Event-Based …eeweb.poly.edu/faculty/yongliu/docs/Meetup.pdfMeetup is an event-based social network in which users can form and join different interest