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Conjoining speeds up information diffusion in overlaying social-physical networks,” arXiv:1112.4002v2 [cs.SI],
, 2011
"... Abstract-We study the diffusion of information in an overlaying social-physical network. Specifically, we consider the following set-up: There is a physical information network where information spreads amongst people through conventional communication media (e.g., face-to-face communication, phone ..."
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Abstract-We study the diffusion of information in an overlaying social-physical network. Specifically, we consider the following set-up: There is a physical information network where information spreads amongst people through conventional communication media (e.g., face-to-face communication, phone calls), and conjoint to this physical network, there are online social networks where information spreads via web sites such as Facebook, Twitter, FriendFeed, YouTube, etc. We quantify the size and the critical threshold of information epidemics in this conjoint social-physical network by assuming that information diffuses according to the SIR epidemic model. One interesting finding is that even if there is no percolation in the individual networks, percolation (i.e., information epidemics) can take place in the conjoint social-physical network. We also show, both analytically and experimentally, that the fraction of individuals who receive an item of information (started from an arbitrary node) is significantly larger in the conjoint social-physical network case, as compared to the case where the networks are disjoint. These findings reveal that conjoining the physical network with online social networks can have a dramatic impact on the speed and scale of information diffusion.
Network Sampling: From Static to Streaming Graphs
, 2013
"... Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorou ..."
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Cited by 12 (3 self)
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Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network science. In this paper, we outline a framework for the general problem of network sampling, by highlighting the different objectives, population and units of interest, and classes of network sampling methods. In addition, we propose a spectrum of computational models for network sampling methods, ranging from the traditionally studied model based on the assumption of a static domain to a more challenging model that is appropriate for streaming domains. We design a family of sampling methods based on the concept of graph induction that generalize across the full spectrum of computational models (from static to streaming) while efficiently preserving many of the topological properties of the input graphs. Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling. Experimental results indicate that our proposed family of sampling methods more accurately preserve the underlying properties of the graph in both static and streaming domains. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.
Massive graph triangulation
- In ACM SIGMOD Conference on Management of Data
, 2013
"... This paper studies I/O-efficient algorithms for settling the classic triangle listing problem, whose solution is a basic operator in deal-ing with many other graph problems. Specifically, given an undi-rected graph G, the objective of triangle listing is to find all the cliques involving 3 vertices ..."
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This paper studies I/O-efficient algorithms for settling the classic triangle listing problem, whose solution is a basic operator in deal-ing with many other graph problems. Specifically, given an undi-rected graph G, the objective of triangle listing is to find all the cliques involving 3 vertices in G. The problem has been well stud-ied in internal memory, but remains an urgent difficult challenge when G does not fit in memory, rendering any algorithm to entail frequent I/O accesses. Although previous research has attempted to tackle the challenge, the state-of-the-art solutions rely on a set of crippling assumptions to guarantee good performance. Motivated by this, we develop a new algorithm that is provably I/O and CPU efficient at the same time, without making any assumption on the input G at all. The algorithm uses ideas drastically different from all the previous approaches, and outperformed the existing com-petitors by a factor over an order of magnitude in our extensive experimentation.
Mining social media with social theories: A survey. SIGKDD Explorations
, 2014
"... The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cult ..."
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The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social me-dia data is big, linked, noisy, highly unstructured and in-complete, and differs from data in traditional data mining, which cultivates a new research field- social media mining. Social theories from social sciences are helpful to explain so-cial phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question- can we ap-ply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale so-cial media data. Social theories have been applied to mining social media. In this article, we review some key social theo-ries in mining social media, their verification approaches, in-teresting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories. 1.
Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis
"... Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale soci ..."
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Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs. Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view. 1.
Understanding Motivations for Facebook Use: Usage Metrics, Network Structure, and Privacy
- In Proc. CHI 2013, ACM
, 2013
"... This study explores the links between motives for using a social network service and numerical measures of that activity. Specifically, it identified motives for Facebook use by employing a Uses and Gratifications (U&G) approach and then investigated the extent to which these motives can be pred ..."
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This study explores the links between motives for using a social network service and numerical measures of that activity. Specifically, it identified motives for Facebook use by employing a Uses and Gratifications (U&G) approach and then investigated the extent to which these motives can be predicted through usage and network metrics collected automatically via the Facebook API. In total, 11 Facebook usage metrics and eight personal network metrics served as predictors. Results showed that all three variable types in this expanded U&G frame of analysis (covering social antecedents, usage metrics, and personal network metrics) effectively predicted motives and highlighted interesting behaviors. To further illustrate the power of this framework, the intricate nature of privacy in social media was explored and relationships drawn between privacy attitudes (and acts) and measures of use and network structure. Author Keywords Uses and gratifications; social network sites; social networks; Facebook; privacy; computer-mediated communication.
Consequences of Connectivity: Characterizing Account Hijacking on Twitter
"... ABSTRACT In this study we expose the serious large-scale threat of criminal account hijacking and the resulting damage incurred by users and web services. We develop a system for detecting large-scale attacks on Twitter that identifies 14 million victims of compromise. We examine these accounts to ..."
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ABSTRACT In this study we expose the serious large-scale threat of criminal account hijacking and the resulting damage incurred by users and web services. We develop a system for detecting large-scale attacks on Twitter that identifies 14 million victims of compromise. We examine these accounts to track how attacks spread within social networks and to determine how criminals ultimately realize a profit from hijacked credentials. We find that compromise is a systemic threat, with victims spanning nascent, casual, and core users. Even brief compromises correlate with 21% of victims never returning to Twitter after the service wrests control of a victim's account from criminals. Infections are dominated by social contagions-phishing and malware campaigns that spread along the social graph. These contagions mirror information diffusion and biological diseases, growing in virulence with the number of neighboring infections. Based on the severity of our findings, we argue that early outbreak detection that stems the spread of compromise in 24 hours can spare 70% of victims.
• Predicting Emerging Social Conventions in Online Social Networks
"... My main research interest is the study of large and complex networks, especially online social networks (OSNs), which includes the measurement and analysis of users ’ activity in OSNs, and the design of systems that can leverage the findings of this analysis. ..."
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My main research interest is the study of large and complex networks, especially online social networks (OSNs), which includes the measurement and analysis of users ’ activity in OSNs, and the design of systems that can leverage the findings of this analysis.
Designing and Deploying Online Field Experiments
- in “Proceedings of the 23rd International Conference on World Wide Web” WWW ’14 ACM
, 2014
"... Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools ..."
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Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from ap-plication code, allowing the experimenter to concisely describe experimental designs, whether common “A/B tests ” and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be im-plemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experi-mental practice.
Information Diffusion in Mobile Social Networks: The Speed Perspective
"... Abstract—The emerging of mobile social networks opens op-portunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals ..."
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Abstract—The emerging of mobile social networks opens op-portunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric k-center problem which is NP-hard, and the best known approximation algorithm for the asymmetric k-center problem has approximation ratio of log ∗ n and time complexity O(n5). Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time. I.
