OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation
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Talk at AAAI2015
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OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation
- 1. OMNI-‐Prop: Seamless Node Classifica/on on Arbitrary Label Correla/on Yuto Yamaguchi† Christos Faloutsos‡ Hiroyuki Kitagawa† †U. of Tsukuba ‡CMU 15/01/29 Yuto Yamaguchi -‐ AAAI2015 1 ? ?
- 2. Node Classifica/on 15/01/29 Yuto Yamaguchi -‐ AAAI2015 2 Find: correct labels of unlabeled nodes ? ?
- 3. Our focus – Label correla/on types 15/01/29 Yuto Yamaguchi -‐ AAAI2015 3 Various label correla5on types Homophily Heterophily Mixed ・ Exis/ng algorithms: prior assump/on needed e.g.) label propaga*on [Zhu+,2003] assumes homophily ・ Our algorithm: no prior assump/on
- 4. Contribu/ons Propose OMNI-‐Prop: a node classifica/on algorithm • Seamless and Accurate – good accuracy on arbitrary label correla/on • Fast – each itera/on is linear on input graph size – convergence guarantee • (Quasi-‐parameter free) -‐ omiZed in this talk for brevity – Just one parameter with default value 1 – No parameter to tune 15/01/29 Yuto Yamaguchi -‐ AAAI2015 4
- 5. ALGORITHM 15/01/29 Yuto Yamaguchi -‐ AAAI2015 5
- 6. Basic Idea 15/01/29 Yuto Yamaguchi -‐ AAAI2015 6 If most of the neighbors of a node have the same label, then the rest also have the same label. ? Most neighbors are the same à the rest is also the same Neighbors have different labels à say nothing ? ?
- 7. How it works? 15/01/29 Yuto Yamaguchi -‐ AAAI2015 7 • sij: How likely node i has label j • tij: How likely the neighbors of node i have label j Calculate two variables recursively male male male unknown male male male male? most friends are males! I am a male s-‐propaga5on t-‐propaga5on s s s s ß aggrega/on of t ß aggrega/on of s t t t t you are probably males see paper for details ? ?
- 8. THEORETICAL RESULTS 15/01/29 Yuto Yamaguchi -‐ AAAI2015 8
- 9. Complexity and Convergence 15/01/29 Yuto Yamaguchi -‐ AAAI2015 9 * K: # labels N: # nodes M: # edges [Theorem 1 - complexity] The time complexity of each iteration of OMNI-Prop is O(K(N+M)) [Theorem 2 - convergence] OMNI-Prop always converges on arbitrary graphs
- 10. Theore/cal connec/on to SSL 15/01/29 Yuto Yamaguchi -‐ AAAI2015 10 Label Propaga/on [Zhu+, 2003] Original graph Twin graph [Theorem 3 - equivalence] The special case of OMNI-Prop is equivalent to LP on twin graph
- 11. EXPERIMENTAL RESULTS 15/01/29 Yuto Yamaguchi -‐ AAAI2015 11
- 12. Experimental Segngs 15/01/29 Yuto Yamaguchi -‐ AAAI2015 12 Datasets Baselines • Label Propaga/on [Zhu+, 2003] • Belief Propaga/on
- 13. Results 15/01/29 Yuto Yamaguchi -‐ AAAI2015 13 OMNI-‐Prop (red line) almost always wins on all datasets upper be[er
- 14. Summary • Proposed OMNI-‐Prop – Seamless NL on arbitrary label correla/on – Fast – (Quasi-‐parameter free) • Theore/cally – Linear on input size for each itera/on – Always converges on arbitrary graphs – special case = LP • Experimentally – Almost always wins on all 5 datasets 15/01/29 Yuto Yamaguchi -‐ AAAI2015 14