KCS-BSSN defines a new community search problem in bipartite spatial-social networks using a (ω, π)-keyword-core and solves it with pruning and indexing techniques shown effective on real and synthetic data.
An o(m) algorithm for cores decomposition of networks
3 Pith papers cite this work. Polarity classification is still indexing.
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The paper proposes the edge-attributed skyline community (ESC) model for bipartite graphs along with peeling and expanding algorithms to find such communities efficiently.
Introduces the NP-hard edge k-core maximization problem via budget-b edge additions and proposes a heuristic algorithm tested on real datasets.
citing papers explorer
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Keyword-based Community Search in Bipartite Spatial-Social Networks (Technical Report)
KCS-BSSN defines a new community search problem in bipartite spatial-social networks using a (ω, π)-keyword-core and solves it with pruning and indexing techniques shown effective on real and synthetic data.
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Skyline Community Search over Edge-Attributed Bipartite Graphs
The paper proposes the edge-attributed skyline community (ESC) model for bipartite graphs along with peeling and expanding algorithms to find such communities efficiently.
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K-Core Maximization through Edge Additions
Introduces the NP-hard edge k-core maximization problem via budget-b edge additions and proposes a heuristic algorithm tested on real datasets.