k-Cliques mining in dynamic social networks based on triadic formal concept analysis

Hao, Fei; Park, Doo-Soon; Min, Geyong; Jeong, Young‐Sik; Park, Jong-Hyuk

doi:10.1016/j.neucom.2015.10.141

Cited by 40 publications

(20 citation statements)

References 21 publications

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“…Technically, the formal context for given graphs is easily constructed via Modified Adjacency Matrix presented in [7][8][9]; (2) (Similarity Evaluation Feature Construction) generally, features are usually needed for evaluation of similarity between graph. In this paper, the formal concept lattices are correspondingly constructed as the feature for further evaluation of similarity between graphs.…”

Section: Figmentioning

confidence: 99%

Similarity Evalution between Graphs: A Formal Concept Analysis Approach

Hao¹,

Sim²,

Park³

et al. 2017

J Inf Process Syst

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Many real-world applications information are organized and represented with graph structure which is often used for representing various ubiquitous networks, such as World Wide Web, social networks, and proteinprotein interactive networks. In particular, similarity evaluation between graphs is a challenging issue in many fields such as graph searching, pattern discovery, neuroscience, chemical compounds exploration and so forth. There exist some algorithms which are based on vertices or edges properties, are proposed for addressing this issue. However, these algorithms do not take both vertices and edges similarities into account. Towards this end, this paper pioneers a novel approach for similarity evaluation between graphs based on formal concept analysis. The feature of this approach is able to characterize the relationships between nodes and further reveal the similarity between graphs. Therefore, the highlight of our approach is to take vertices and edges into account simultaneously. The proposed algorithm is evaluated using a case study for validating the effectiveness of the proposed approach on detecting and measuring the similarity between graphs.

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Section: Figmentioning

confidence: 99%

Similarity Evalution between Graphs: A Formal Concept Analysis Approach

Hao¹,

Sim²,

Park³

et al. 2017

J Inf Process Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Formally, problems in graph theory have also been analyzed by formal concept analysis; for example, K-clique of dynamic social networks are mined from formal concept lattice [9,10]. From the formal concept analysis point of view, if a graph is converted into a formal context, then notions of formal concept analysis can be used to express problems in graph theory, if fact, social networks graphs are converted into a special formal context K = (U, A, I) [9,10], in which U = A = V are vertices of the graphs and I means the edges between two vertices; accordingly, the K-cliques problem of the social networks is expressed by K-formal concepts of the special formal context K = (U, A, I).…”

Section: Related Workmentioning

confidence: 99%

“…From the formal concept analysis point of view, if a graph is converted into a formal context, then notions of formal concept analysis can be used to express problems in graph theory, if fact, social networks graphs are converted into a special formal context K = (U, A, I) [9,10], in which U = A = V are vertices of the graphs and I means the edges between two vertices; accordingly, the K-cliques problem of the social networks is expressed by K-formal concepts of the special formal context K = (U, A, I). Theoretically, many interesting notions and mining methods have been proposed in formal concept analysis, such as frequent itemsets, closed frequent itemsets, maximal frequent itemsets, expressive generalized itemsets, and disjunctive closed itemsets [11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by our earlier works [9,10,23], this paper pioneers the study of the bases of maximal cliques in social networks. We firstly present the concept of base of maximal clique, then we point out that the maximal clique can be formed based on the detected bases.…”

Section: Contributionsmentioning

confidence: 99%

“…The relationship between the concept lattices of the original formal context K (i.e., C(K)) and induced formal context K (i.e, C(K )) is shown in Figure 2. According to the findings about the equivalence between equiconcept and clique [9,10], it is clear that the maximal cliques in G include {2, 3, 5}, {2, 5, 6}, {4, 7}, {1}. Interestingly, these maximal cliques are aggregated then represented as the relevant concepts in Figure 2.…”

Section: Detection Approachmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting the Formation of Maximal Cliques in Social Networks

2017

Self Cite

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Abstract:In social networking analysis, there exists a fundamental problem called maximal cliques enumeration(MCE), which has been extensively investigated in many fields, including social networks, biological science, etc. As a matter of fact, the formation principle of maximal cliques that can help us to speed up the detection of maximal cliques from social networks is often ignored by most existing research works. Aiming to exploit the formation of maximal cliques in social networks, this paper pioneers a creative research issue on the detection of bases of maximal cliques in social networks. We propose a formal concept analysis-based approach for detecting the bases of maximal cliques and detection theorem. It is believed that our work can provide a new research solution and direction for future topological structure analysis in various complex networking systems.

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An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

Novais

Maciel

Souza

et al. 2021

Concurrency and Computation

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Algorithms for the extraction of formal concepts are widely studied in several areas of knowledge, such as finance, health, and statistics. However, these algorithms require high‐performance processing due to their combinatorial characteristics. In this work, an Open computing language (OpenCL)‐based Brute Force algorithm is proposed and evaluated for formal concept extraction on heterogeneous architectures (CPU+GPU and CPU+FPGA). The CPU+GPU architecture presents higher performance and scalability than other architectures when our Brute Force algorithm processes high dimensional contexts with many objects and attributes. Our parallel approach shows performance results up to 18× better than a smarter sequential algorithm called Data‐Peeler. Moreover, our Brute Force algorithm running on CPU+GPU architecture has greater energy efficiency, reaching at least 1.79× more operations per energy consumption than other algorithms on different architectures explored in this work.

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k-Cliques mining in dynamic social networks based on triadic formal concept analysis

Cited by 40 publications

References 21 publications

Similarity Evalution between Graphs: A Formal Concept Analysis Approach

Similarity Evalution between Graphs: A Formal Concept Analysis Approach

Exploiting the Formation of Maximal Cliques in Social Networks

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

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