omni-prop: seamless node classification on arbitrary label correlation
TRANSCRIPT
OMNI-‐Prop: Seamless Node Classifica/on on Arbitrary Label Correla/on
Yuto Yamaguchi† Christos Faloutsos‡
Hiroyuki Kitagawa†
†U. of Tsukuba ‡CMU
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Node Classifica/on
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Find: correct labels of unlabeled nodes
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Our focus – Label correla/on types
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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
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
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ALGORITHM
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Basic Idea
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If most of the neighbors of a node have the same label, then the rest also have the same label.
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Most neighbors are the same à the rest is also the same
Neighbors have different labels à say nothing
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How it works?
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• 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
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THEORETICAL RESULTS
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Complexity and Convergence
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* 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
Theore/cal connec/on to SSL
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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
EXPERIMENTAL RESULTS
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Experimental Segngs
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Datasets
Baselines • Label Propaga/on [Zhu+, 2003] • Belief Propaga/on
Results
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OMNI-‐Prop (red line) almost always wins on all datasets
upper be[er
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
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