Link-Sign Prediction in Dynamic Signed Directed Networks

International audienceMany real-world applications can be modeled as signed directed graphs wherein the links between nodes can have either positive or negative signs. Social networks can be modeled as signed directed graphs where positive/negative links represent trust/distrust relationships between users.In order to predict user behavior in social networks, several studies have addressed the link-sign prediction problem that predicts a link sign as positive or negative. However, the existing approaches do not take into account the time when the links were added which plays an important role in understanding the user relationships. Moreover, most of the existing approaches require the complete network information which is not realistic in modern social networks. Last but not least, these approaches are not adapted for dynamic networks and the link-sign prediction algorithms have to be reapplied each time the network changes.In this paper, we study the problem of link-sign prediction by combining random walks for graph sampling, Doc2Vec for node vectorization and Recurrent Neural Networks for prediction. The approach requires only local information and can be trained incrementally. Our experiments on the same datasets as state-of-the-art approaches show an improved prediction

A Feature Based Model for Negative Sign Prediction in Signed Social Networks

People hold all kinds of positive and negative feelings for one another. Social networking online serves as a platform for showcasing such relationships, whether friendly or unfriendly, like or dislike, trust or distrust, cooperation or dissension. These types of interactions result in the creation of signed social networks (SSNs). The sentiments among social individuals are complexity and diversity, and the relationships between them include being friendly and hostile. The positive (“friendly”, “like” or “trust”) or negative (“hostile”, “dislike” or “distrust”) sentiments in the relations can be modeled as signed connections or links. The missing relations or sentiments between individuals are always worthy of speculation. Hence, we need to predict negative sign prediction. Although negative signs typically dominate the positive signs in various analytical decisions in most real applications, it cannot be directly propagated between users like positive signs. The study on negative sign prediction is still in its early stages. There is a difference between the value of negative signs and the availability of these links in real data sets. It is therefore normal to analyze whether one can automatically predict negative signs from the widely available social network data. In this thesis, we propose a novel negative sign prediction model which includes negative sign related features from various categories to predict negative sign in signed social network. An extensive set of experiments is carried out on real-world social network datasets which demonstrate that the proposed model outperforms the existing method in predicting negative signs in terms of accuracy and F1 score(is a measure of a test’s accuracy) by 3% ∼ 4% and 5% ∼ 15% respectively

Vote Prediction Models for Signed Social Networks

Voting is an integral part of the decision-making mechanism in many communities. Voting decides which bills become laws in parliament or users become administrators on Wikipedia. Understanding a voter's behaviour and being able to predict how they will vote can help in selecting better and more successful policies or candidates. As votes tend to be for or against a particular agenda, they can be intuitively represented by positive or negative links respectively in a signed network. These signed networks allow us to view voting through the lens of graph theory and network analysis. Predicting a vote translates into predicting the sign of a link in the network. The task of sign prediction in signed networks is well studied and many approaches utilize social theories of balance and status in a network. However, most conventional methods are generic and disregard the iterative nature of voting in communities. Therefore this thesis proposes two new approaches for solving the task of vote prediction in signed networks. The first is a graph combination method that gathers features from multiple auxiliary graphs as well as encoding balance and status theories using triads. Then, it becomes a supervised machine learning problem which can be solved using any general linear model. Second, we propose a novel iterative method to learn relationships between users to predict votes. We quantify a network's adherence to status theory using the concept of agony and hierarchy in directed networks. Analogously, we use the spectral decomposition of the network to measure its balance. These measures are then used to predict the votes that comply the most with the social theories. We implement our approaches to predict votes in the elections of administrators in Wikipedia. Our experiments and results on the Wiki-RfA dataset show that the iterative models perform much better than the graph combination model. We analyse the impact of the voting order on the performance of these models. Furthermore, we find that balance theory represents votes in Wikipedia elections better than status theory

A Model of Consistent Node Types in Signed Directed Social Networks

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 201