A classification approach to link prediction in multiplex online ego-social networks

Rezaeipanah, Amin; Ahmadi, Gholamreza; Matoori, Samaneh Sechin

doi:10.1007/s13278-020-00639-6

Cited by 36 publications

(13 citation statements)

References 74 publications

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“…Different network associations have different practical significance. e main existing forms of network associations can be divided into the following three categories [25], as shown in Figures 4-6 (1) Nonoverlapping network community structure: this kind of community refers to that there are no common nodes between communities or subgraphs, as shown in Figure 4 (2) Overlapping network communities: it mainly refers to that some nodes in the network belong to two or more different communities at the same time, as shown in Figure 5 (3) Hierarchical network community: this kind of community refers to the existence of hierarchy or hierarchical structure in the community, as shown in Figure 6 2.2. Student Group Relations in the Complex Network.…”

Section: Student Group Identification Based Onmentioning

confidence: 99%

Group Relationship Mining of College Students Based on Predictive Social Network

Liu

2022

Security and Communication Networks

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With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

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Section: Student Group Identification Based Onmentioning

confidence: 99%

Group Relationship Mining of College Students Based on Predictive Social Network

Liu

2022

Security and Communication Networks

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“…In essence, data classification investigates the relations between feature variables (i.e., inputs) and output variables. Classification methods have been used in a broad range of applications such as customer target marketing [1,2], medical disease diagnosis [3][4][5], speech and handwriting recognition [6][7][8][9], multimedia data analysis [10,11], biological data analysis [12], document categorization and filtering [13,14], and social network analysis [15][16][17]. Classification algorithms typically contain two steps, the learning step and the testing step.…”

Section: Classification Methodsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

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The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

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“…It is important to study link prediction in multi-layer networks. Multi-layer networks consist of several layers with the number of same nodes in each layer [6]. The information from these layers may be used to predict missing links in a layer.…”

Section: Link Prediction In Multi-layer Networkmentioning

confidence: 99%

“…In [20], a decision tree classification model is proposed to link prediction in a multiplex collaboration network with three layers. In [6], the supervised classification model is used to link prediction in a two-layer network including Twitter and Foursquare. Weight networks may provide more information than unweight networks in which each link has a specific weight [21].…”

Section: Link Prediction In Multi-layer Networkmentioning

confidence: 99%

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Yang

Peng

et al. 2022

Concurrency and Computation

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Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user' on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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A classification approach to link prediction in multiplex online ego-social networks

Cited by 36 publications

References 74 publications

Group Relationship Mining of College Students Based on Predictive Social Network

Group Relationship Mining of College Students Based on Predictive Social Network

Ontology-Based Feature Selection: A Survey

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

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