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DISCO: Influence Maximization Meets Network Embedding and Deep Learning
As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency.
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector.
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