A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Abstract

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

“…In time series-based temporal link prediction, researchers have proposed various methods to enhance the accuracy of temporal link prediction by obtaining the local similarity of nodes in different ways. Huang et al [17] used specific time granularity to treat temporal network data sets into snapshots. They proposed an improved gravity model with second-order neighbors, denoted by gravity (GR), to compute the score matrix in each static network snapshot.…”

“…All of the above time series-based temporal link predictions [17][18][19] follow the framework processing rules shown in Figure 1. First, the temporal network represents different snapshots at different periods.…”

“…A large time granularity L can provide more edge information for snapshots, but it can also destroy the accuracy of reference scores for similarity among nodes during the prediction process. The time series-based temporal link prediction method [17][18][19] mentioned above enhances the expressive capacity of network snapshots and improves the computational speed of the algorithm by considering the local information of node. Regardless, large time granularity may not guarantee the algorithm's accuracy [31].…”

Graph Embedding Method Based on Biased Walking for Link Prediction

“…Link prediction is an important part of complex network analysis [1], which aims at predicting missing, spurious, or new links in the current structure of the network by using the structure information and attribute information of a given network [2]. Link prediction plays an important role in social network analysis [3,4], network reconstruction [5], and network evolution mechanisms [6][7][8]. In addition, link prediction in the theoretical analysis assists in comprehending the mechanism of propagation and diffusion of information [9].…”