Abstract:
We consider the enumeration of dense substructures (maximal cliques) from an uncertain graph. For parameter 0 <; α <; 1, we define the notion of an a-maximal clique in an...Show MoreMetadata
Abstract:
We consider the enumeration of dense substructures (maximal cliques) from an uncertain graph. For parameter 0 <; α <; 1, we define the notion of an a-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of a-maximal cliques possible within a (uncertain) graph. We present an algorithm to enumerate a-maximal cliques whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 29, Issue: 3, 01 March 2017)
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- IEEE Keywords
- Index Terms
- Experimental Evaluation ,
- Social Networks ,
- Interaction Network ,
- Exhaustive Search ,
- Correction Algorithm ,
- Collaborative Network ,
- Probability Threshold ,
- Size Threshold ,
- Vertices ,
- Recursive Algorithm ,
- Random Graph ,
- Output Size ,
- Set Of Graphs ,
- Recursive Method ,
- Input Graph ,
- Path Search ,
- Existence Probability ,
- Runtime Analysis ,
- Runtime Complexity ,
- Scale-free Model
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Experimental Evaluation ,
- Social Networks ,
- Interaction Network ,
- Exhaustive Search ,
- Correction Algorithm ,
- Collaborative Network ,
- Probability Threshold ,
- Size Threshold ,
- Vertices ,
- Recursive Algorithm ,
- Random Graph ,
- Output Size ,
- Set Of Graphs ,
- Recursive Method ,
- Input Graph ,
- Path Search ,
- Existence Probability ,
- Runtime Analysis ,
- Runtime Complexity ,
- Scale-free Model
- Author Keywords