Abstract

A maximal clique ( ${MC}$ ) is a complete subgraph satisfying that no other cliques can take it as their proper subgraph. Given an uncertain graph, the $top\text{-}K\,\,{MC}\text{s}$ enumeration problem studies how to return $k {MC}\text{s}$ with the highest rank value. Existing algorithms rank ${MC}\text{s}$ according to their probabilities, thus usually return ${MC}\text{s}$ with higher probabilities but less number of vertices, and fail to return large ${MC}\text{s}$ that convey more useful information. Considering this problem, this paper studies the problem of enumerating $top\text{-}K\,\,{MC}\text{s}$ . Our approach returns $k {MC}\text{s}$ with the most number of vertices satisfying that their probabilities $\geq \alpha $ , where each ${MC}$ is called an $\alpha $ - ${MC}$ , and computing $k$ largest $\alpha $ - ${MC}\text{s}$ is called as $(k,\alpha)$ - ${MC}\text{s}$ . We propose an efficient $(k,\alpha)$ - ${MC}\text{s}$ enumeration algorithm, $Top\text{-}KMC$ , which works in three steps, including partition, enumeration and verification. Here, partition means that we compute the set $\mathcal {M}$ of all ${MC}\text{s}$ without considering the probability information, as if the graph is partitioned into a set of subgraphs. Enumeration means that we compute $\alpha $ - ${MC}\text{s}$ from each ${MC}$ of $\mathcal {M}$ . As each such subgraph is an ${MC}$ , the cost of computing common neighbors for finding $\alpha $ - ${MC}\text{s}$ can be reduced. Verification means that we need to verify whether an $\alpha $ - ${MC}$ is a subgraph of another $\alpha $ - ${MC}$ . If not, it is an $\alpha $ - ${MC}$ ; otherwise, it is a useless $\alpha $ - ${MC}$ and should be removed. We further propose an optimized algorithm $Top\text{-}KMC+$ to reduce both time and space by merging the above three steps into a whole step. The experimental results on real datasets show that both $Top\text{-}KMC$ and $Top\text{-}KMC+$ can return $k$ largest $\alpha $ - ${MC}\text{s}$ efficiently.

Highlights

  • Graphs have been widely used to describe the complex relationships between different entities

  • A maximal clique (MC) in a graph G is a complete subgraph satisfying that no other cliques can take it as their proper subgraph [13]–[16]

  • MC enumeration in an uncertain graph is used to find from the underlying graph all MCs satisfying that each one has probability ≥ α, where α is a threshold specified by users [17], and each MC is called an α-MC

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Summary

INTRODUCTION

Graphs have been widely used to describe the complex relationships between different entities. Users may be more interested in the MC {v5, v6, v7, v8} which has the most vertices Considering this problem, we propose to return k largest MCs satisfying that their probabilities ≥ α, which is denoted as (k, α)-MCs. For example, if using our approach and limiting α = 0.1, the result of top-1 α-MC w.r.t. Figure 1 is {v5, v6, v7, v8}. To make a further improvement, we propose an optimized algorithm Top-KMC+, which combines the three steps of Top-KMC into a whole step, and only maintains a result set of k ·2tmax to store the largest α-MCs, where tmax is the largest truss number [36] of the graph.

PRELIMINARIES
DATASETS
VIII. CONCLUSION
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