Welcome !

Welcome to the webpage of the satellite "social influence in networks" !

What our satellite is about?

Our behaviors are significantly affected by others. We seek advice when choosing a college, taking a job offer and relocating to a new place. We consider ratings when downloading an APP, purchasing a merchant, watching a movie and booking a hotel. The increasing development of social media technology allows individuals to share and exchange their opinions faster than ever, which embeds us into a social network tightly connecting each individual. Consequently, there have been intensive investigations on the social influence in networks, including the dynamics of the information spreading, the effects of network structure and the heterogeneous influence ability and susceptibility among individuals, the prediction and the control of the influence, and so on.

The aim of this satellite is to bring together scientists and researchers in multiple areas to present and discuss their recent works on social influence. The speakers are from network science, computer science and research team in IT company, characterized by very diverse backgrounds. We believe a joint discussion and communication, as planned to be performed at this satellite, can bring more insights into fundamental issues of this topic. More importantly, it will stimulate research on problems in real social systems, bringing solutions and applications to the industry in a near future.

Call for Contributions

We still have a few slots for a 10-minute presentation with slides. Participants are invited to submit via EasyChair an abstract of maximum 1 page in PDF format, specifying title, author(s), affiliation(s) and e-mail address(es). Contributions are evaluated on a rolling base, until all slots are filled up (but no later than April 30th). Please makes sure to submit your abstract soon, if you plan on attending and contributing to the workshop.

Areas of Interest include but are not limited to the following focused topics:

  • Dynamics of opinion/influence spreading
  • Machine learning methods in spreading & social influence
  • Leadership identification
  • Cascade modeling and popularity prediction
  • Role of community in spreading
  • Network structure in social influence
  • The adoption process in social media
  • Influence maximization
  • Visualization of cascades diffusion

Invited Speakers (In Alphabetic Order Of First Name)

  • Boleslaw K. Szymanski

    Social Cognitive Networks Academic Research Center, USA

    Title: Impact of Social Influence on Society Stability

    Abstract: TBA

  • Filippo Radicchi

    Indiana University, USA

    Title: Maximum entropy sampling in complex networks

    Abstract: Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. In this talk, I will introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. I'll show that the method is effective for various processes on different substrates. I'll further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.

  • Flaviano Morone

    City College of New York, USA

    Title: TBA

    Abstract: TBA

  • Giovanni Luca Ciampaglia

    Indiana University, USA

    Title: TBA

    Abstract: TBA

  • Peng He

    Tencent Company, China

    Title: TBA

    Abstract: TBA

    Bio: Paul He is a data scientist at Tencent WXG Group. He got his master degree from Xi'an Jiaotong university in 2009 and joined Tencent since then . He has been engaged in social media data mining-related projects for 8 years. He focuses on network representation learning, human behavioral modeling, location -based social network(LBSN), and machine learning, and his work has been applied on cascade prediction, vital nodes identification, community detection of online social network, socail recommendation, and so on. He has co-authored a paper in AAAI2015. He has attended and been the keynote speaker in CCCN2017, ICML2014 MLChina workshp, ICDM2014 Spatial Big Data Mining and Visualization workshop, GITC2014.

  • Przemysław Kazienko


    Title: TBA

    Abstract: TBA

    Bio: TBA

  • Quan Li

    The Hong Kong University of Science and Technology, Hong Kong, China

    Title: Visualization of Information Diffusion and Prediction in WeChat Platform and A Visual Analytics Approach for Understanding Egocentric Intimacy Network Evolution and Impact Propagation in MMORPGs

    Abstract: As a new type of social networking service, WeChat has already become ubiquitous in people’s daily mobile communication. Tracking in the pulse of WeChat information propagation is important. For corporations, it enables them to get feedback of user coverage, information diffusion patterns, and gain insight into how to improve and market better. Also, the abundance of information and opinions from diverse sources in WeChat platform help them tap into the wisdom of crowds, understand the retweeting behaviors and aid in making more informed decisions. Meanwhile, WeChat generates enormous digital data archives recording various user activities, introducing a proliferation of opportunities to understand users’ communication behaviors. Analyzing these behaviors not only helps find the common communication patterns adopted by the public, but more importantly facilitate the detection of anomalous users who are potential threats to society. In the first topic, we will answer the following questions: which articles of WeChat official accounts need to be selected and monitored based on their potential popularity and how to visually understand their propagation? What features may result in their potential great popularities?

    Massively Multiplayer Online Role-playing Games (MMORPGs) feature a large number of players socially interacting with one another in an immersive gaming environment. A successful MMORPG should engage players and meet their needs to achieve different categories of gratifications. Research on the evolution of player social interaction network and the dynamics of inter-player intimacy could provide insights into players' gratification-oriented behaviors in MMORPGs. Such understanding could in turn guide game designs for better engaging existing players and marketing strategies for attracting newcomers. Conventional dynamic network analysis may help investigate game-based social interactions at the macroscopic level. However, current dynamic network visualization techniques mainly focus on illustrating topological changes of the entire network, which are unsuitable for analyzing player-specific social interactions in the virtual world from an egocentric perspective. In general, game designers and operators find it difficult to analyze the way players with different gratification needs may interact with one another and the consequences on their relationships with direct ties, using a decentralized social graph with complicated time-varying structures. In this paper, we present MMOSeer, a visual analytics system for exploring the evolution of egocentric player intimacy network. MMOSeer focuses on the relationship between a player (ego) and his/her directly-linked friends (alters). We follow a user-centered design process to develop the system with game analysts and apply novel visualization techniques in conjunction with well-established algorithms to depict the evolution of intimacy egocentric network. We also derive a centrality change metric to infer how the impact of changes in an ego's interactive behaviors may propagate through the intimacy network, reshaping the structure of the alters' social circles at both micro and macro levels. Finally, we validate the usability of MMOSeer by discovering different user interaction patterns and the corresponding ego-network structural changes in a real-world gameplay dataset from a commercial MMORPG.

  • Shenghua Liu

    Institute of Computing Technology,Chinese Academy of Sciences, China

    Title: TBA

    Abstract: TBA

    Bio: TBA

  • Yanqing Hu

    Sun Yat-Sen University, China

    Title:Local Determines Global: Identification and Quantification of Influential Spreaders in Large Scale Social Networks

    Abstract: Measuring and optimizing the spread of influence in big-data online social networks are important for designing efficient viral marketing strategies. As the viral spread of information on social network is a global process, it is commonly believed that measuring the influence of nodes and optimizing viral spreading would require the whole network information. By mapping the spreading dynamics onto bond percolation in statistical physics, we find that in many stochastic spreading events, the information spreading happens in only one of two well separated phases: a locally confined phase with a small number of influenced nodes, and a global viral spreading phase with a fixed fraction of whole network nodes that is invariant with respect to initial spreaders and realizations. The global and local phases are clearly separated, which allows us to distinguish between them by a small characteristic infection size. Thus, without the need of information about the global network structure, any nodes’ global influence on the entire network can be accurately measured based on this local characteristic infection size. This motivates an efficient algorithm with constant time complexity on the NP problem of best seed spreaders selection, whose performance remarkably close to the true optimum.

    Bio: Dr. Yanqing Hu is Associate Professor of the School of data and computer science in Sun Yat-sen Univ of China. He received the Ph.D. degree from Beijing Normal Univ. in 2011. His current research interests focus on percolation theory and application on complex network.


Schedule

TBA


Venue

TBA


Organizers

  • Lingling Yi,Tencent, China
  • Tao Jia, Southwest University, China
  • Linyuan Lü,Hangzhou Normal University, China
  • Huawei Shen, Chinese Academy of Science, China
  • Xiaofan Wang, Shanghai Jiao Tong University, China