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Motivated by applications in social network community analysis, we introduce a new clustering paradigm termed motif clustering. Unlike classical clustering, motif clustering aims to minimize the number of clustering errors associated with both edges and certain higher order graph structures (motifs) that represent “atomic units” of social organizations. Our contributions are two-fold: We first introduce...
In recent years, it has become more and more popular to recommend friends on the location-based social network (LBSN), which is combined with the user's behavior in the real world. LBSN has three attributes including temporal, spatial and social correlation. However, the combination situation of the three cannot be solved in previous algorithms. For instance, the problem of recommending friends with...
Recently, social event recommendation, which is to recommend a list of upcoming events to a user, has attracted a lot of research interests. In this paper, we first construct a heterogeneous graph to express the interactions among different types of entities in event-based social network. Based on the constructed graph, we propose a novel recommendation algorithm called reverse random walk with restart...
In this paper, we study the problem of performing multi-label classification on networked data, where each instance in the network is assigned with multiple labels and the connections between instances are driven by various casual reasons. Networked data extracted from social media or web pages may not reflect the relationship between users in real life accurately. By mining the links that actually...
In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the Vote Watch Europe Website for the whole 7th term (2009-2014), by considering voting similarities between Members of the EP...
In location-based social networks, the current friend recommendation algorithms just take a relatively single factor into account without comprehensive evaluations. To solve this problem, we design a framework - Multiple Heterogeneous Social Network (MHSN) according to users' profiles, check-in records and interests. Based on this framework, we propose a friend recommendation model which consider...
Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information...
Nowadays, with the rapid development of hardware and network technologies, various types of smart devices are released by different vendors, resulting in the emergence of Internet of Things (IoT). However, most of the existing approaches for IoT device management are designed in a centralized way, whose efficiency meets challenges recently because of the large scale of heterogeneous device modules...
In this paper we introduce a direct approach for Block modeling in multi-relational networks inspired by the Pajek-Approach. In this article the direct Block modeling-method is presented for two-relational networks and evaluated statistically compared to indirect approaches. In addition we apply the method to the "Krackhardt`s High-tech Managers" dataset to show the feasibility of the approach...
Sina-Microblog, the earliest and biggest microblogging service in China, has become one of the most popular media in information propagation. In order to gain insights into the topological and information diffusing characteristics of microblogging network in China, we crawled Sina-Microblog for about 3 months and obtain the trace of its topology and topics. Compared with other online social networks,...
Rumors, new ideas, and attitudes spread through groups of people via interpersonal communication. One approach to simulate these diffusion processes is to generate random or stylized networks. Normally, random, small world, and scale-free network generation algorithms are used to that end. However, the structure of the network is an important parameter for the pattern of diffusion. Therefore, the...
This paper describes an automatic text analysis of values contained in the Enron email dataset that seeks to explore the potential to apply value patterns to cluster a social network. Two hypotheses are posed: individuals communicate more frequently with other individuals who share similar value patterns than with individuals with different value patterns; and people who communicate more frequently...
As a typical social media in Web 2.0 era, blogs have become more and more important to information diffusion. Different from the traditional news, the information spread on blogs is primarily driven by users and their relations. According to this phenomenon, this paper addresses the novel problem of measuring the influence of social structures on information diffusion. This paper extracts the hidden...
Measuring user's prestige and interaction preference is an important issue for social network site implementer. To address this problem, an empirical study based on xiaonei.com is conducted. In this work, we introduce the "interaction prestige" metrics based on the observation of user's interaction features. Users with distinct levels of "interaction prestige" are grouped into...
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