Rumour Source Identification in Static Network

Submitted by: Vishakha Malik, Prof. Maheswari S

Vishakhamalik1213@gmail.com, maheswari.s@vit.ac.in

Abstract:

Rumour is a vital problem for modern techniques of communication. A piece of unauthenticated information travels around the social network creating chaos. In this study, based upon an integrated window prototype model network, N number of nodes are generated. In the study first, the time-varying networks are reduced to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual, we use a reverse dissemination traversal algorithm to specify a set of suspects in the source. Third, to determine the real source from the suspects, we use a novel microscopic rumour spreading model to calculate the data counts for each suspect. We consider the one who gets the largest count estimate as to the real source.

We create built-in dataset other than the cases, if a user sends existing information in the network it will not allow it to pass on and the fault information creator will block in the network and won’t be allowed to send more information. Thus, the approaches help in building reliability in the system.

Keywords:

Time-integration window, Source identification, Reverse dissemination topology etc.

Introduction

Rumors effect is more harmful than a wound. The social network plays vital role in spreading and curing the network from such raw-full information. The study in [3] focus on minimizing the effects of rumor keeping in mind it’s not a trivial task and rumors related to breaking news. The novel approach that has different automation for identifying the information characteristics using neural networks. The study helps in alleviating the shift problems by simulating cross topics emerging in their study. In [4] the author has discussed about techniques for detecting rumor in the network. The paper provides essential knowledge about different types of data and methods of handling the datasets. The survey done in the study for most used techniques are

usually based on binary or multi-class classification problem. They have defined about the study of text content, user information exploitation. Different structures like attribute-based or structure-based features. Approaches based on propagation path or hand-crafted user features. In 2015 wang and terano introduced social graph in traditional methods to model the interaction between the user and end system, other graphics they also worked on heterogeneous network which contains multiple types of nodes and edges. In 2018 an RNN model was designed by Zhang to find rumor through creator’s contents, subjects and relationships. Also, they have discussed about the frame work, external textual information, multi-task learning in detection of rumor. In the chapter they have discussed about the traditional approaches for rumor detection and modern approaches (wireless techniques). Time-varying social network influenced the information diffusion [7]. They have focused on security states of Individual, observation on time-varying social networks, reverse dissemination method, Propagation model etc. for rumor identification. This study requires the prior knowledge about the network and study related to rumor identification algorithms for source identify in the network.

• The aim of the study is to identify the fake message source in the static network and eliminate it from the source network.

Problem Statement:

The rumor source detection problem is defined as follows: A message x is defined a set of w pieces of words {w1, w2, …, wn}. w1 is the set words in x which is sent from one node N to another or communication. Each x in N node has its neighbors and its attributes on which the neighbor nodes are formed. Further, neighbor node can send the data to its neighbors and make a group of nodes. Each node has the attribute like unique ID, time line,

range, distance, message box, data receive table, fake message table, count hood etc. The detection of rumor is done as: Given X message with W set words and N node, the rumor source detection task is determined to find the source on bases of count-hood (C) [7].

2. Existing work

Rumors effect is more harmful than a wound. The social network plays vital role in spreading and curing the network from such raw-full information. The study in [3] focus on minimizing the effects of rumor keeping in mind it’s not a trivial task and rumors related to breaking news. The novel approach that has different automation for identifying the information characteristics using neural networks. The study helps in alleviating the shift problems by simulating cross topics emerging in their study.

In the paper “A Rumor Spreading Model with Control Mechanism on Social Networks” the author has proposed a novel susceptible infected model for controlling the rumor velocity in the network through anti- rumor. They have also calculated the method for shortening the time delay between the rumor starting and anti-rumor start. The study helped in providing methods for controlling the flow of rumor between the channels. The techniques used for setting up the network is undirected graph with edges and nodes.

Discussed modeling and simulation of gene- regulatory network (GRNs) which is used in modern computational biology investigation. But it is difficult to automate the method in automated inference in reverse-engineering for dynamic purpose. In the study Considering the mathematical initials it uses relative weight and activators with single modeling. The fault in the methods under degree prior known topology information the exhibit had been considered on different predictive performance. Alexandru Mizeranschi and his team used a multiscale modeling and simulation called MAPPER which belongs to the biological research.

In [4] the author has discussed about techniques for detecting rumor in the network. The paper provides essential knowledge about different types of data and methods of handling the datasets. The survey done in the study for most used techniques are usually based on binary or multi-class classification problem. They have defined about the study of text content, user information exploitation. Different

structures like attribute-based or structure-based features. Approaches based on propagation path or hand-crafted user features. In 2015 wang and terano introduced social graph in traditional methods to model the interaction between the user and end system, other graphics they also worked on heterogeneous network which contains multiple types of nodes and edges. In 2018 an RNN model was designed by Zhang to find rumor through creator’s contents, subjects and relationships. Also, they have discussed about the frame work, external textual information, multi-task learning in detection of rumor.

The study for observation on infected nodes been done using Probability and limitation functions for nominating the local rumors in the susceptible infected model which is also constructed using maximum a posteriori (MAP). The probability of n node increases with the distance between the two suspects. They have also used polya’s urn model probability theory in all the dominion.

A double identity rumor spreading model is proposed by XuefanDonga, YijunLiub, ChaoWud, YingLiana, DaishengTang [6] and have discussed about the root causes of rumor and their psychological theory. In double identity model they have considered two repudiates, first is rumor spreading identity U1 and real-world identity U2. They have created sets belong to different ranges like activities, connectivity degree, Influential degree, Immunity, Forgetting rate. They have also worked on four assumptions which are rumor controller, rumor creator and normal user influential degree is measured. In which the rumor controller is smaller than creator and the number of these two groups are smaller than normal users. Second, only the modes with active state can receive the information and rumor controller and creator hold the active state. Third, the identities of rumor controller and creator can be recovered and infected. Forth there is a time lag between rumor controller and creator.

In the chapter they have discussed about the traditional approaches for rumor detection and modern approaches (wireless techniques). Time-varying social network influenced the information diffusion [7]. They have focused on security states of Individual, observation on time-varying social networks, reverse dissemination method, Propagation model etc. for rumor identification. They work on the maximum likely-hood count as

the complementary approach. By counting the spreading patterns in nodes for general ranking of each node [8]. An additional information is also inserted for forming the set of directed pairs of neighboring nodes. For finding the most likely rumor sources based on the Markov chain tree theorem. In further study they have worked on the variants problem for fraction of pairs of node [8].

The rumor is defined as an unverified statement from a single or multiple source across the meta data. The rumor is defining as true, false and unresolved. The key intent is to offer a stance to the amount and type of work conducted in the area [9].

3. Proposed System:

The proposed framework depends on sources distinguishing proof technique, time-shifting informal organization by lessening the static arrangement organize. Each totaling all edges and hubs present in a period incorporation window and by utilizing reverse scattering procedure to limit the size of the sources. The opposite dispersal process circulates duplicates of gossipy tidbits conversely from the client whose states have been resolved dependent on different perception upon the system. The person who can all the while getting all duplicates of gossipy tidbits from the contaminated clients should be suspects of the genuine sources. The proposed framework depends on sources ID technique, time-differing interpersonal organization by lessening the static grouping system. Each accumulating all edges and hubs present in a period mix window and by utilizing reverse scattering procedure to limit the size of the sources. The converse spread procedure appropriates duplicates of bits of gossip contrarily from the client whose states have been resolved dependent on different perception upon the system. The person who can at the same time get all duplicates of gossipy tidbits from the contaminated clients should be suspects of the genuine sources.

To recognizing fake sources in a social network, expect a fundamental activity in confining the mischief realized by them through the favourable segregate of the sources. Regardless, the short-lived assortment in the topology of relational associations and the advancing unique methods challenge our customary source recognizing

verification procedures are considered in static frameworks. We decrease the time-fluctuating frameworks to a movement of static frameworks by introducing an integrated window. By then instead of surveying every individual in regular techniques, we grasp an opposite dispersing framework to show a lot of suspects of the certifiable tattle source.

4. Implementation

4.1. Network Formation

It forms a network in this module using a network within a network user can send any information. But users are considered as a node which is having neighbours and holds a unique ID. The system will be continuously running on the top of the nodes we need to change the network from dynamic to static using the received time for every individual by using system time. Creating nodes (communication channels in the network). Forming neighbours in the range using heap tress. Minimum weight range is generated by subtracting the range from the distance and maximum weight by adding distance and range.

4.2. Novel Source Identification:

This module works on the time-varying network. Example: The neighbourhood of individual moving over a geographical space evolves over time and interaction between the individual appears and disappears in an online social network. Time-varying social network is defined as an ordered stream of interaction between individuals. The interaction depends on the time progress and its keep on changing as per the time variations. Every node has its unique ID for sending the data on the channel. And IDs have generated anonymously. In this module, the essence of social networks lies in its time-varying nature. For example, the neighbourhood of individuals moving over a geographic space strengthens over time (i.e., physical mobility), and the interaction between the individuals appears and disappears in online social networks (i.e., online/offline). Time-varying social networks are defined by an ordered stream of interactions between individuals. The interaction structure keeps changing as per time progresses. Examples can be found in both face-to-face interaction networks, and online social networks.

The temporal nature of such networks has a deep influence on information spreading on top of them. The rumour spread has affected by duration, sequence, and re-receiving the message. The time-varying decreased in networks because of time integrating window in static networks.

4.3. Reverse Dissemination Process:

It sends the messages received by an individual back to the origin from where it was sent. The reverse dissemination method is used to sending the copies of the rumour along the reversed dynamic connection from observed nodes to exhaust all spreading paths leading to the observation. The reversed model is inspired by the Jordon method but it has a different functionality it depends on the time-varying social network which involves the physical mobility and the offline status of the user.

Pseudocode:

Input:

A set of observed word w = {w1,w2,...,wn}, a set of infection times of the observed nodes {n1,n2, ...,nn}, a threshold α, and a threshold Xmax.

Initialize: A set of suspects U = ∅, and X1 = ... = Xn = X if w is a wavefront, otherwise X = max{X1,X2, ...,Xn}.

for (X starts from 1 to a maximum value Xmax) do for (oi: i starts from 1 to n)

do if (wi has not disseminated the rumour) then propagate the rumour from user wi separately and independently at time x + x − xi.

end

end

for (M: any node in the entire network)

do if (user u received n different rumor from x) then Calculate the maximum likelihood L (C) for user u; Add user u into the set X.

end

end

if (|U| ≥ αC) then Keep the first αC suspects with large maximum likelihoods in C, and delete all the other suspects. Stop.

end

end

Output: A set of suspects U.

4.4. Microscopic Rumor Spreading method:

The module is used as a bottleneck of identifying the rumour sources. The method is based on node centralization which ignores the propagation probabilities between nodes. A rumour spreading model is defined to model the rumour spreading in the time-varying social network. In this, every user maintains two types of table one, received message table another is a fake data table. The first table contains the message, message Id, sender name, etc. The fake table contains fake data and respective attributes. The fake message user identified from in the channel is traversed in the revered order.

After receiving on the sender part the send data is counted. It declares the one who is having the highest count as a fake source and remove it from the network.

5. Result and discussion

We summarize the result of our experiments in pictorial form. In the study, the future work of [11] has continued which is identifying rumour sources in the time-varying social network. In the study, we have worked on multiple observation in a static network on discrete timing in time-integrated windows using reverse dissemination algorithm. In, addition we have included a built-in data to check on the data-stealing by any node. The output shows the communication between the node, a message broadcast, targeting false information, blocking the fake source and check on data.

Figure-1 Shows how any nodes you want to create for the communication channel.

Figure-2 Shows the communication between five nodes and their neighbours.

Figure-4 Show the alert prompt of fake message.

Figure-5 Show the prompt message of network removal.

Conclusion:

The evaluation has carried out as a prototype model in static social networks with time-varying topology. The experiment results detect the fake source in the channel by 60% investigating an area in various time-varying windows. The node transfers the information to its neighbour nodes and further they transfer it to other creating a multi communication in the channel. Thus, the actual result equalized to the desired.

In, further work we could make the network stronger using supervised learning techniques or bi-direction algorithm in the time-varying windows additionally working with the real dataset (e.g. PHEME). However, the observation can consider as a graph, analysis charts. Future work may lead to strengthening the data between the channels.

References:

[1] The influence of network topology on reverse- engineering of gene-regulatory networks Alexandru Mizeranschi, Noel Kennedy, Paul Thompson, Huiru Zheng, and Werner Dubitzky, ELSEVIER, 2014.

[2] A Rumor Spreading Model with Control Mechanism on Social Networks Ya-Qi Wang,∗ Xiao-Yuan Yang, and Jing Wang,2014.

[3] Detecting breaking news rumors of emerging topics in social media SarahA.Alkhodair, StevenH.H.Ding, Benjamin C.M. Fung, JunqiangLiuc.

[4] Rumor Detection on social Media: Datasets, Methods and opportunities Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu Alibaba Group, US Bellevue, WA, USA, 2019.

[5] Rooting out the Rumor Culprit from Suspects Wenxiang Dong∗,Wenyi Zhang∗ and Chee Wei Tan 978-1-4799-0446-4/13/$31.00 ©2013 IEEE.

[6] A double- Identity rumor spreading model XuefanDonga, YijunLiub, ChaoWud, YingLiana, DaishengTang 2019 Elsevier.

[7] Identifying Propagation Source in Time- Varying Networks © Springer Nature Switzerland AG 2019 J. Jiang et al., Malicious Attack Propagation and Source Identification, Advances in Information Security 73, https://doi.org/10.1007/978-3-030-02179-5_10.

[8] Temporally Agnostic Rumor Source Detection, Ankit Kumar DOI: 10.1109/TSIPN.2017.2668141, IEEE 2016.

[9] Rumor Detection Using Machine Learning Techniques on Social Media © Springer Nature Singapore Pvt. Ltd. 2019 S. Bhattacharyya et al. (eds.), International Conference on Innovative Computing and Communications, Lecture Notes in Networks and Systems 56, https://doi.org/10.1007/978-981-13-2354-6_23.

[10]Towardsearlyidentificationofonlinerumorsbasedonlongshorttermmemorynetworks YahuiLiu, XiaolongJin, HuaweiShen https://doi.org/10.1016/j.ipm.2018.11.003, 2018 Elsevier Ltd.

[11] Rumor Source Identification in Social Networks with Time-varying Topology, DOI 10.1109/TDSC.2016.2522436, IEEE.2018