Semantic Proximity Search on Graphs with Metagraph-based Learning Yuan Fang 1 Wenqing Lin 1 Vincent Zheng 2 Min Wu 1 Kevin Chang 23 Xiao-Li Li 1 Problem: Semantic Proximity Search on Heterogeneous Graph Insights: Metagraphs to “Explain” Different Semantic Classes 1 Institute for Infocomm Research, Singapore 2 Advanced Digital Sciences Center, Singapore 3 University of Illinois at Urbana-Champaign, USA Object/Attribute Type Which 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 ܟ: weight for metagraph ܕ௫௬ i:# times ݕ,ݔco-occur in instances of metagraph ܕ௫ i:# times ݔoccurs in instances of metagraph Each example is a triplet: for query ݍ, ݔis 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 Overall Framework Matching Metagraphs Existing method Symmetry-based matching o Backtracking DFS search o Node by node until an entire matched instance is found o Fail to leverage symmetric components o Many metagraphs are symmetric o 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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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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