Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

Personalized Expert Recommendation: Models and Algorithms

Many large-scale information sharing systems including social media systems, questionanswering sites and rating and reviewing applications have been growing rapidly, allowing millions of human participants to generate and consume information on an unprecedented scale. To manage the sheer growth of information generation, there comes the need to enable personalization of information resources for users — to surface high-quality content and feeds, to provide personally relevant suggestions, and so on. A fundamental task in creating and supporting user-centered personalization systems is to build rich user profile to aid recommendation for better user experience. Therefore, in this dissertation research, we propose models and algorithms to facilitate the creation of new crowd-powered personalized information sharing systems. Specifically, we first give a principled framework to enable personalization of resources so that information seekers can be matched with customized knowledgeable users based on their previous historical actions and contextual information; We then focus on creating rich user models that allows accurate and comprehensive modeling of user profiles for long tail users, including discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile. In particular, this dissertation research makes two unique contributions: First, we introduce the problem of personalized expert recommendation and propose the first principled framework for addressing this problem. To overcome the sparsity issue, we investigate the use of user’s contextual information that can be exploited to build robust models of personal expertise, study how spatial preference for personally-valuable expertise varies across regions, across topics and based on different underlying social communities, and integrate these different forms of preferences into a matrix factorization-based personalized expert recommender. Second, to support the personalized recommendation on experts, we focus on modeling and inferring user profiles in online information sharing systems. In order to tap the knowledge of most majority of users, we provide frameworks and algorithms to accurately and comprehensively create user models by discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile, with each described shortly as follows: —We develop a probabilistic model called Bayesian Contextual Poisson Factorization to discover what users are known for by others. Our model considers as input a small fraction of users whose known-for profiles are already known and the vast majority of users for whom we have little (or no) information, learns the implicit relationships between user?s known-for profiles and their contextual signals, and finally predict known-for profiles for those majority of users. —We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users, and demonstrate how user’s opinion bias can be exploited to recommend other users with similar opinion in social networks. — We study how a user’s topical profile varies geo-spatially and how we can model a user’s geo-spatial known-for profile as the last step in our dissertation for creation of rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating user contexts into the two-layered hierarchical user model for better representation of user’s geo-topic preference by others

Network alignment on big networks

In the age of big data, multiple networks naturally appear in a variety of domains, such as social network analysis, bioinformatics, finance, infrastructure and so on. Network alignment, which aims to find the node correspondences across different networks, can integrate multiple networks from different sources into a world-view network. By mining such a world-view network, one may gain considerable insights that are invisible if mining different networks separably. Networks as one common data type, share the well-known 4Vs characteristics of big data, including variety, veracity, velocity and volume, each of which brings unique challenges to the big network alignment task. Specifically, the variety characteristic of big networks depicts the rich information associated with multiple networks. Many prior network alignment methods find the node correspondences merely based on network structures while inevitably ignoring the rich node and/or edge attributes of networks. In the meanwhile, conventional methods often assume the alignment consistency among the neighboring node pairs, which could be easily violated due to the disparity among various networks. Despite the emergence of the sites and tools that enable to link entities, there still exist the bottlenecks of collecting the networked data, such as the privacy issues in social networks. Thus, real-world networks are often noisy and incomplete with missing edges. However, it still remains a daunting task on how to deal with the incompleteness and analyze the robustness of network alignment owing to the veracity characteristic. The velocity of big networks indicates that real-world networks are often dynamically changing. The dynamics behind multiple networks may benefit network alignment from the temporal information of nodes and edges in addition to the static structural information of networks. Yet, how to design the dynamic alignment model still remains an open problem. Given the sheer volume of large-scale networks but relatively limited computational resources, the at least quadratic complexity of many prior network alignment methods is not scalable especially when aligning networks with a large number of nodes and edges. In this way, the efficiency issue has become a fundamental challenge of big network alignment. The theme of my Ph.D. research is to address the above challenges associated with the 4Vs characteristics and align big networks. Note that we consider volume as an overarching goal so we can align big networks efficiently. First (for variety), to leverage attribute information of networks, we develop a family of algorithms FINAL that optimize the alignment consistency in terms of network structures and attributes and achieve an up to 30% improvement in terms of the alignment accuracy over the comparison methods without attributes. We also develop a novel alignment method that displace node representations to be more comparable through non-rigid point set registration. Moreover, to address network disparity issue, we design an encoder-decoder architecture NetTrans that learns network transformation functions in a hierarchical manner. Besides, we design a relational graph convolutional network based model with an adaptive negative sampling strategy to strike a balance between alignment consistency and disparity. This developed method named NextAlign achieves an at least 3% performance improvement over the best competitor. Second (for veracity), we hypothesize that network alignment and network completion mutually benefit each other and develop an effective algorithm based on multiplicative update that outperforms baseline methods on incomplete networks. In addition, we provide a robustness analysis of network alignment against structural noise. Last (for velocity), we design a representation learning model on dynamic network of networks which can leverage temporal information underlying networks and is applied for dynamic network alignment task

Social search in collaborative tagging networks : the role of ties

[no abstract