Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.

An expert finding system allows a user to type a simple text query and retrieve names and contact information of individuals that possess the expertise expressed in the query. This paper proposes a novel approach to expert finding in large enterprises or intranets by modeling candidate experts (persons), web documents and various relations among them with so-called expertise graphs. As distinct from the stateof-the-art approaches estimating personal expertise through one-step propagation of relevance probability from documents to the related candidates, our methods are based on the principle of multi-step relevance propagation in topicspecific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi-step relevance propagation improve over the baseline one-step propagation based method in almost all cases.

The huge amount of knowledge in web communities has motivated the research interests in threaded discussions. The dynamic nature of threaded discussions poses lots of challenging problems for computer scientists. Although techniques such as semantic models and structural models have been shown to be useful in a number of areas, they are inefficient in understanding threaded discussions due to three reasons: (I) as most of users read existing messages before posting, posts in a discussion thread are temporally dependent on the previous ones; It causes the semantics and structure to be coupled with each other in threaded discussions; (II) in online discussion threads, there are a lot of junk posts which are useless and may disturb content analysis; and (III) it is very hard to judge the quality of a post. In this paper,

Social Network Marketing techniques employ pre-existing social networks to increase brands or products awareness through word-of-mouth promotion. Full understanding of social network marketing and the potential candidates that can thus be marketed to certainly offer lucrative opportunities for prospective sellers. Due to the complexity of social networks, few models exist to interpret social network marketing realistically. We propose to model social network marketing using Heat Diffusion Processes. This paper presents three diffusion models, along with three algorithms for selecting the best individuals to receive marketing samples. These approaches have the following advantages to best illustrate the properties of real-world social networks: (1) We can plan a marketing strategy sequentially in time since we include a time factor in the simulation of product adoptions; (2) The algorithm of selecting marketing candidates best represents and utilizes the clustering property of real-world social networks; and (3) The model we construct can diffuse both positive and negative comments on products or brands in order to simulate the complicated communications within social networks. Our work represents a novel approach to the analysis of social network marketing, and is the first work to propose how to defend against negative comments within social networks. Complexity analysis shows our model is also scalable to very large social networks.

Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.

Online forums represent one type of social media that is particularly rich for studying human behavior in information seeking and diffusing. The way users join communities is a reflection of the changing and expanding of their interests toward information. In this paper, we study the patterns of user participation behavior, and the feature factors that influence such behavior on different forum datasets. We find that, despite the relative randomness and lesser commitment of structural relationships in online forums, users’ community joining behaviors display some strong regularities. One particularly interesting observation is that the very weak relationships between users defined by online replies have similar diffusion curves as those of real friendships or co-authorships. We build social selection models, Bipartite Markov Random Field (BiMRF), to quantitatively evaluate the prediction performance of those feature factors and their relationships. Using these models, we show that some features carry supplementary information, and the effectiveness of different features vary in different types of forums. Moreover, the results of BiMRF with two-star configurations suggest that the feature of user similarity defined by frequency of communication or number of common friends is inadequate to predict grouping behavior, but adding node-level features can improve the fit of the model.

Diffusion occurs in various contexts and generally involves a network of entities and interactions between entities. Through these interactions, some property, e.g. information, ideas, etc., is spread through the network. This paper presents a general model of diffusion in dynamic networks. We simulate the diffusion of evacuation warnings in multiple network structures under various model settings and observe the proportion of evacuated nodes. The network dynamics occur as the result of the diffusion where nodes may leave the network after receiving the warning. We use the model to explore how the network structure, seeding strategy, network trust, and trust distribution affect the diffusion process. The effectiveness of the diffusion is a function of the network structure and seeding strategy used in delivering the initial broadcast. The simulation results reveal interesting observations on the effects of network trust and distribution of trust in the network.

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

Diffusion occurs in various contexts and generally involves a network of entities and interactions between entities. Through these interactions, some property, e.g. information, ideas, etc., is spread through the network. The network may become dynamic as entities in the network interact and information, ideas, etc. flow through the network. This paper presents a general model of diffusion in dynamic networks. We use the model to examine how network structure, seeding strategy, and population inhomogeneity as defined with trust, affects the diffusion process. We simulate an evacuation scenario where the network structure represents a network of households. There are multiple sources that initiate the broadcasts of evacuation warnings and the goals are for the households to propagate the message and perform evacuation. The network dynamics observed are the result of the diffusion, where households may leave the network some time after receiving the warning. The results provide interesting observations on the effects of trust asymmetry and trust differentials. When we introduce population inhomogeneity using trust, the diffusion was more effective. The network structure and the seeding strategy used in delivering the initial broadcast also affect the effectiveness of the diffusion. 1.

Abstract—Online discussion boards are an important medium for collaboration. The goal of our work is to understand how messages and individual discussants contribute to Q&A discussions. We present a novel network model for capturing information roles of messages and discussants, and show how we identify useful answers to the initial question. We first classify information seeking or information providing roles of messages, such as question, answer or acknowledgement. We also identify user intent in the discussion as an information seeker or a provider. We capture such role information within a reply-to discussion network, and identify messages that answer seeker questions and how answeres are acknowledged. Message influences are analyzed using B-centrality measures. User influences across different threads are combined with message influences. We use the combined score in identifying the most useful answer in the thread. The resulting ranks correlate with human provided ranks with an MRR score of 0.67.