Evidential Link Prediction Based on Group Information

Abstract

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An Evidential Method for Multi-relational Link Prediction in Uncertain Social Networks

“…This imprecision impacts directly the network structure and therefore the outcomes of the analysis. Indeed, we show in previous works [14,15] the relevance of handling uncertainty whether within the structure or throughout the link prediction process. However, we treated uniplex social networks where there are only uni-relational links between the actors.…”

Evidential link prediction in social networks based on structural and social information

“…However, the effect of group memberships is not taken into account, only structural local information is treated. In [46], we studied the case where the analyzed links already have prior evidence, the task is to update the current knowledge depending on the graph state. In this work, our proposed method takes into consideration these two cases.…”

“…Yet, such conceptualization does not consider uncertainty that results from imperfect data or unreliability of the tools used for the construction of the network. In this respect, we suggest to encode the uncertainty degrees on the edges level by adopting the belief function theory [46,47]. Each edge uv has assigned a basic belief assignment denoted by m uv defined on Θ uv = {E uv , ¬E uv }, E uv depicts the event that the link between u and v exists and ¬E uv means that it is absent.…”

Mining user behavior in location-based social networks

“…Because the community structure encloses itself different structural and social properties as social ties, triadic closure, structural holes, and others, much of researchers focused on the use of social theory on link prediction have put their attention on better use the community information to improve the link prediction accuracy. Therefore, based on the primary works of Zheleva et al (2008), Soundarajan and Hopcroft (2012), and Valverde-Rebaza and Lopes (2012a), a considerable amount of works using community information to enhance the link prediction have been proposed (HOSEINI; HASHEMI; HAMZEH, 2012; CANNISTRACI; ALANIS-LOBATO; RAVASI, 2013;KEMAL;TSUYOSHI et al, 2014;MALLEK et al, 2015;DAMINELLI et al, 2015;DING et al, 2016;MA et al, 2016;KUANG;YU, 2016;CAIYAN;BISWAS;BISWAS, 2017). Considering the fact that social grouping is a natural behavior of users in OSNs and that social groups can be associated directly with community structure (YANG; LESKOVEC, 2015), we will use the concepts of social group and community interchangeably throughout this thesis.…”

“…Several link prediction methods use a variety of information sources available in OSNs. Some of the information sources commonly used are: i) homophily (MCPHERSON;SMITH-LOVIN;COOK, 2001;CHANG et al, 2014;FARALLI;STILO;VELARDI, 2015), ii) social ties (LÜ; ZHOU, 2010; SOCIEVOLE; RANGO; MARANO, 2013;KLEINBERG, 2014;XU et al, 2017) and iii) communities (ZHELEVA et al, 2008;SOUNDARAJAN;HOPCROFT, 2012;LOPES, 2012a;HOSEINI;HASHEMI;HAMZEH, 2012; CANNIS-TRACI; ALANIS-LOBATO; RAVASI, 2013;KEMAL;TSUYOSHI et al, 2014;MALLEK et al, 2015;DAMINELLI et al, 2015;DING et al, 2016;MA et al, 2016;KUANG;YU, 2016;CAIYAN;BISWAS;BISWAS, 2017). Because some researches have shown that community information can drastically improve the link prediction accuracy XU, 2012;, most of the research efforts have been directed to better explore this information source.…”