Hidden Community Detection on Two-Layer Stochastic Models: A Theoretical Perspective

Bao, Jialu; He, Kun; Xin, Xiaodong; Selman, Bart

doi:10.1007/978-3-030-59267-7_31

Cited by 1 publication

(2 citation statements)

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“…In [2], the authors prove that for a two-layer block model network, the modularity of a layer increases if we apply ReduceWeight on all communities in the other layer. Now we show the different influences that the two situations mentioned in Theorem 1 have to the followup detection.…”

Section: Theoretical Analysismentioning

confidence: 99%

“…According to Lemma 2 in [2], if the layer weakening method reduces a bigger percentage of outgoing edges than internal edges in layer 𝑙, the modularity of layer 𝑙 increases after the weakening. Therefore, the modularity of layer 2 after weakening when 𝐶 1 and 𝐶 2 are detected separately is higher than that when the two communities are regarded as one:…”

Section: Theoretical Analysismentioning

confidence: 99%

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Uncovering the Local Hidden Community Structure in Social Networks

Wang¹,

Li²,

He³

2021

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Hidden community is a useful concept proposed recently for social network analysis. Hidden communities indicate some weak communities whose most members also belong to other stronger dominant communities. Dominant communities could form a layer that partitions all the individuals of a network, and hidden communities could form other layer(s) underneath. These layers could be natural structures in the real-world networks like students grouped by major, minor, hometown, etc. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some "broken communities" that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer's structure to reveal the hidden layers. Eventually, we repeat the whole process and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection. CCS Concepts: • Mathematics of computing → Graph algorithms; • Information systems → Data mining.

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Section: Theoretical Analysismentioning

confidence: 99%

Section: Theoretical Analysismentioning

confidence: 99%