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Semantic Proximity Search on Graphswith Metagraph-based Learning
Yuan Fang1 Wenqing Lin1 Vincent Zheng2 Min Wu1 Kevin Chang23 Xiao-Li Li1
Problem: Semantic Proximity Search on Heterogeneous Graph
Insights: Metagraphs to “Explain” Different Semantic Classes
1 Institute for Infocomm Research, Singapore2 Advanced Digital Sciences Center, Singapore3 University of Illinois at Urbana-Champaign, USA
Object/Attribute
TypeWhich users are close to /related to Bob?
Family? (Alice)Classmates? (Tom)
On a “typed” object graph that captures users and their attributes on a social network:
Family[Bob & Alice]
Classmates[Kate & Jay, Bob & Tom]
Close friends[Kate & Alice] [Kate & Jay]
Offline
Online
mining metagraphs
matching metagraphs (ie,
finding instances)indexing
training
testing
Definition of Proximity Basic Learning Model
Training
Proximity of two nodes on graph
: weight for metagraph
i : # times , co-occur in instances of metagraphi : # times occurs in instances of metagraph
Each example is a triplet: for query , is ranked before y.
Pairwise learning to rank
Objective function
Dual-Stage Training
Expensive to process/match all metagraphs Yet not all metagraphs are useful
identify seed metagraphs
learn with seed metagraphs
re-learn with seed + selected
metagraphs
select more metagraphs
based on weights of seed metagraphs and their structural relationship with other metagraphs
Ove
rall
Fra
mew
ork
Matching Metagraphs
Existing method
Symmetry-based matching
o Backtracking DFS searcho Node by node until an entire
matched instance is foundo Fail to leverage symmetric
components
o Many metagraphs are symmetrico Avoid redundant computation
Main Results
Datasets: • College & Coworkers (labelled on LinkedIn)• Family & Classmate (rule on Facebook)
Baselines:• MGP: metagraph-based proximity (ours)• MPP: metapath-based proximity• MGP-U: all metagraphs have uniform weights• MGP-B: only use the best metagraph• SRW: supervised random walk
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