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Article overview
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Attribute Truss Community Search | Xin Huang
; Laks V.S. Lakshmanan
; | Date: |
1 Sep 2016 | Abstract: | Recently, community search over graphs has attracted significant attention
and many algorithms have been developed for finding dense subgraphs from large
graphs that contain given query nodes. In applications such as analysis of
protein protein interaction (PPI) networks, citation graphs, and collaboration
networks, nodes tend to have attributes. Unfortunately, previously developed
community search algorithms ignore these attributes and result in communities
with poor cohesion w.r.t. their node attributes. In this paper, we study the
problem of attribute-driven community search, that is, given an undirected
graph $G$ where nodes are associated with attributes, and an input query $Q$
consisting of nodes $V_q$ and attributes $W_q$, find the communities containing
$V_q$, in which most community members are densely inter-connected and have
similar attributes.
We formulate our problem of finding attributed truss communities (ATC), as
finding all connected and close k-truss subgraphs containing $V_q$, that are
locally maximal and have the largest attribute relevance score among such
subgraphs. We design a novel attribute relevance score function and establish
its desirable properties. The problem is shown to be NP-hard. However, we
develop an efficient greedy algorithmic framework, which finds a maximal
$k$-truss containing $V_q$, and then iteratively removes the nodes with the
least popular attributes and shrinks the graph so as to satisfy community
constraints. We also build an elegant index to maintain the known $k$-truss
structure and attribute information, and propose efficient query processing
algorithms. Extensive experiments on large real-world networks with
ground-truth communities shows the efficiency and effectiveness of our proposed
methods. | Source: | arXiv, 1609.0090 | Services: | Forum | Review | PDF | Favorites |
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