A Framework of Algorithms: Computing the Bias and Prestige of Nodes in Trust Networks

A trust network is a social network in which edges represent the trust relationship between two nodes in the network. In a trust network, a fundamental question is how to assess and compute the bias and prestige of the nodes, where the bias of a node measures the trustworthiness of a node and the prestige of a node measures the importance of the node. The larger bias of a node implies the lower trustworthiness of the node, and the larger prestige of a node implies the higher importance of the node. In this paper, we define a vector-valued contractive function to characterize the bias vector which results in a rich family of bias measurements, and we propose a framework of algorithms for computing the bias and prestige of nodes in trust networks. Based on our framework, we develop four algorithms that can calculate the bias and prestige of nodes effectively and robustly. The time and space complexities of all our algorithms are linear w.r.t. the size of the graph, thus our algorithms are scalable to handle large datasets. We evaluate our algorithms using five real datasets. The experimental results demonstrate the effectiveness, robustness, and scalability of our algorithms

On the Troll-Trust Model for Edge Sign Prediction in Social Networks

In the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the troll-trust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple probabilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.Comment: v5: accepted to AISTATS 201

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure

Three Essays on Trust Mining in Online Social Networks

This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships. In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks. In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines. In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines