Concept Stability Based Isolated Maximal Cliques Detection in Dynamic Social Networks

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordIn: Green, Pervasive, and Cloud Computing, edited by Z. Yu, C. Becker, G. Xing. GPC 2020: International Conference on Green, Pervasive, and Cloud Computing, 13 - 15 November Xi'an, ChinaAs the network security gradually deviates from the virtual environment to the real environment, the security problems caused by abnormal users in social networks are becoming increasingly prominent. These abnormal users usually form a group which can be regarded as an isolated network. This paper aims to detect the isolated maximal cliques from a dynamic social network for identifying the abnormal users in order to cut off the source of fake information in time. By virtue of concept stability, an isolated maximal clique detection approach is proposed. Experimental results shown that the proposed algorithm has a high F-measure value for detecting the isolated maximal cliques in social network.National Natural Science Foundation of China (NSFC)Natural Science Basic Research Plan in Shaanxi Province of ChinaFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi ProvinceEuropean Union Horizon 202

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe booming of Social Internet of Things (SIoT) has witnessed the significance of graph mining and analysis for social network management. Online Social Networks (OSNs) can be efficiently managed by monitoring users behaviors within a cohesive social group represented by a maximal clique. They can further provide valued social intelligence for their users. Maximal Cliques Problem (MCP) as a fundamental problem in graph mining and analysis is to identify the maximal cliques in a graph. Existing studies on MCP mainly focus on static graphs. In this paper, we adopt the Formal Concept Analysis (FCA) theory to represent and analyze social networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA and Dec-FCA, that can be applicable to OSNs for detecting the maximal cliques and characterizing the dynamic evolution process of maximal cliques in OSNs. Extensive experimental results are conducted to investigate and demonstrate the correctness and effectiveness of the proposed algorithms. The results reveal that our algorithms can efficiently capture and manage the evolutionary patterns of maximal cliques in OSNs, and a quantitative relation among them is presented. In addition, an illustrative example is presented to verify the usefulness of the proposed approach.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Fundamental Research Funds for the Central Universitie

Learning Concept Interestingness for Identifying Key Structures from Social Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.Fundamental Research Funds for the Central Universitie

Translating Networks: Assessing correspondence between network visualisation and analytics

Network interpretation is a widespread practice in the digital humanities, and its exercise is surprisingly flexible. While there is now a wide variety of uses in different fields from social network analysis to the study of document circulation metadata or literature and linguistic data, many projects highlight the difficulty of bringing graph theory and their discipline into dialogue. Fortunately, the development of accessible software, followed by new interfaces, sometimes with an educational dimension, has been accompanied in recent years by a critical reflection on our practices, particularly with regard to visualisation. Yet, it often focuses on technical aspects. In this paper, we propose to shift this emphasis and address the question of the researcher’s interpretative journey from visualisation to metrics resulting from the network structure. Often addressed in relation to graphical representation, when it is not used only as an illustration, the subjectivity of translation is all the more important when it comes to interpreting structural metrics. But these two things are closely related. To separate metrics from visualisation would be to forget that two historical examples of network representation, Euler (1736) and Moreno (1934), are not limited to a graphic reading (the term “network” itself would only appear in 1954 in Barnes’ work). In the first case, the demonstration was based on a degree centrality measurement whereas in the second case the author made the difference between “stars” and “unchosen” individuals while qualifying the edges as inbound and outbound relationships. This is why this paper propose to examine the practice of visual reading and metrics-based analysis in a correspondence table that clarifies the subjectivity of the translation while presenting possible and generic interpretation scenarios