Dense Subgraphs Summarization: An Efficient Way to Summarize Large Scale Graphs by Super Nodes

Lu, Yu; Jiang, Bo; Guo, Kai; Zhou, Tie Hua

doi:10.1007/978-3-030-60796-8_45

Cited by 3 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike other compression methods [11], the DSS method [16] is not designed to optimise the size of the summary. Thus it is, generally speaking, not the most efficient in terms of compression rates.…”

Section: Dense Subgraph Summarizationmentioning

confidence: 99%

“…This property can be measured by the concept of visibility introduced in [15] and defined in Section 3. In [16], the authors propose the Dense Subgraph Summarization method (DSS), a lossless compression scheme which addresses this issue, allowing a direct access to the dense subgraphs in the summary. However, this method requires the computation of some dense components of the Graph.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method requires the computation of some dense components of the Graph. In [16], the dense components are formally defined as quasi-cliques (cf. Definition 1) and the computation is done by solving the Quasi-Clique Enumeration Problem (cf.…”

Section: Introductionmentioning

confidence: 99%

“…DSS [16] uses the Quick algorithm [9] to solve the QCE problem. This algorithm performs a Depth-First Search on the solution space tree along with several pruning techniques.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quasi-Clique Mining for Graph Summarization

Castillon

Baste

Haddad

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Several graph summarization approaches aggregate dense sub-graphs into super-nodes leading to a compact summary of the input graph. The main issue for these approaches is how to achieve a high compression rate while retaining as much information as possible on the original graph structure, within the summary, without having to decompress it. These approaches necessarily involve an algorithm to mine dense structures in the graph such as quasi-clique enumeration algorithms. In this paper, we focus on improving this mining algorithms for the specific task of graph summarization. We first introduce a new pre-processing technique to speed up this mining step. Then, we adapt existing clique enumeration algorithms to dense sub-graph mining and apply them to graph summarization. Our extensive experimental study, on both synthetic and real-world graphs, show that our preprocessing technique allows an important speed up of the mining process, making it very useful for quasi-clique enumeration in general. Also, when applied to graph summarization, our approach improves significantly the execution time and the compression rate while maintaining a good retention of the original graph information especially those related to dense subgraphs.

show abstract

Section: Dense Subgraph Summarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…DSS [16] uses the Quick algorithm [9] to solve the QCE problem. This algorithm performs a Depth-First Search on the solution space tree along with several pruning techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quasi-Clique Mining for Graph Summarization

Castillon

Baste

Haddad

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The study of dense subgraphs has been of high interest in research community for many decades. There have been numerous works on graphlet mining [19,54,63], on complete dense subgraphs such as maximal cliques [13, 18, 23-26, 29, 46, 53, 69], maximal bicliques [3,43,52], and graph summarization based on maximal cliques and quasi-cliques [76]. There exist many different types of degree-based incomplete dense subgraphs other than the quasi-clique, such as k-core [9,17,27,38], k-truss [20], and k-plex [10,21,22].…”

Section: Other Work On Dense Subgraphsmentioning

confidence: 99%

Mining Largest Maximal Quasi-Cliques

Sanei-Mehri

Das

Hashemi

et al. 2021

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating quasi-cliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top- k degree-based quasi-cliques and make the following contributions: (1) we show that even the problem of detecting whether a given quasi-clique is maximal (i.e., not contained within another quasi-clique) is NP-hard. (2) We present a novel heuristic algorithm K ernel QC to enumerate the k largest quasi-cliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, followed by growing subgraphs around these kernels, to arrive at quasi-cliques with the required densities. (3) Experimental results show that our algorithm accurately enumerates quasi-cliques from a graph, is much faster than current state-of-the-art methods for quasi-clique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.

show abstract