Analyzing negative ties in social networks: A survey

Abstract

Online social networks are a source of sharing information and maintaining personal contacts with other people through social interactions and thus forming virtual communities online. Social networks are crowded with positive and negative relations. Positive relations are formed by support, endorsement and friendship and thus, create a network of well-connected users whereas negative relations are a result of opposition, distrust and avoidance creating disconnected networks. Due to increase in illegal activities such as masquerading, conspiring and creating fake profiles on online social networks, exploring and analyzing these negative activities becomes the need of hour. Usually negative ties are treated in same way as positive ties in many theories such as balance theory and blockmodeling analysis. But the standard concepts of social network analysis do not yield same results in respect of each tie. This paper presents a survey on analyzing negative ties in social networks through various types of network analysis techniques that are used for examining ties such as status, centrality and power measures. Due to the difference in characteristics of flow in positive and negative tie networks some of these measures are not applicable on negative ties. This paper also discusses new methods that have been developed specifically for analyzing negative ties such as negative degree, and h * measure along with the measures based on mixture of positive and negative ties. The different types of social network analysis approaches have been reviewed and compared to determine the best approach that can appropriately identify the negative ties in online networks. It has been analyzed that only few measures such as Degree and PN centrality are applicable for identifying outsiders in network. For applicability in online networks, the performance of PN measure needs to be verified and further, new measures should be developed based upon negative clique concept.Ó 2015 Production and hosting by Elsevier B.V. on behalf

How to Measure Influence in Social Networks?

“…• Influence measures based on walks between pair of users -These types of measures provide relative power or status of user in a network by accounting all length paths between pair of nodes [26]. • Link topological ranking measures -According to [8], most centrality metrics do not consider the variation of nodes in their calculation: these metrics consider that all nodes contribute equally to their calculation.…”

“…These metrics are used to find the most central and influential node in the network. The centrality metrics for finding the centrality of the node depend on the structural properties of the network and make use of flows to analyse these characteristics [16], [26], [28]. Table 3.…”

“…Katz centrality allows not only direct links received by a user but also popularity or status of users sending links to him to be included in his score. Further, the status of each, who has link with these users in turn, should also be used for calculating scores in social network [26]. The equation that defines it is as follows: ⃗ = (( − ) −1 − ) ⃗ , Where, I is the identity matrix, ⃗ is a vector of size n (n is the number of nodes) consisting of ones.…”

“…denotes the transposed matrix of A and ( − ) −1 denotes matrix inversion of the term ( − ). Through Katz measure, most influential node or individual positive tie network can be found who has connections with most of the other users and can influence or affect other users with his decisions or activities [26]. This measure is similar to PageRank algorithm and eigenvector centrality.…”

“…The Hubbel measure has structural as well as functional significance. The structural significance of index is in identifying cliques and functional significance is in computation of status [26]. This measure is similar to Katz centrality, the Katz measure uses an identity matrix (each node is connected to itself) while the Hubble measure does not.…”