Social networks

In this paper, we focus on the issue of price discrimination based upon position of individuals in the social network. The problem of price discrimination based upon position in the social network has received significant attention in recent years. However, the question of interest in the research has been to characterize and identify optimal prices and profits in deterministic networks and the complexity of computing optimal prices.

We provide evidence that networks representing social interactions, as measured and archived by electronic communication systems, such as friendship, collaboration, co-authorship, web, peer-to-peer and other kindred networks, exhibit strong hyperbolicity in the sense of Gromov (4-point delta being much smaller than the graph diameter). We outline how one may exploit hyperbolicity to reduce computations on such graphs massively.

The study of the spread of influence through a social network has a long history in the social sciences. The first studies focused on the adoption of medical and agricultural innovations, later marketing researchers investigated the word-of-mouth diffusion process as an important mechanism by which information can reach large populations, possibly influencing public opinion, driving new product market share and brand awareness.

As an increasing amount of social interaction moves on-line, it becomes possible to study phenomena that were once essentially invisible: how our social networks are organized, how groups of people come together and attract new members, and how information spreads through society. With computational and mathematical ideas, we can begin to map the rich social landscape that emerges, filled with hot spots of collective attention, and behaviors that cascade through our networks of social connections.

Machine learning researchers focus on two distinct learning scenarios for structured graph and network data. In one scenario, the domain consists of a population of structured examples (e.g., chemical compounds) and we can reason about learning algorithms asymptotically, as the number of structured examples increases. In the other scenario, the domain consists of a single, potentially infinite-sized network (e.g., Facebook). In these single network domains, an increase in data corresponds to acquiring a larger portion of the underlying network.

This project analyzes networks of actors and statements concerning climate change at two levels, the national and the global. Each level has its own two-mode actor-statement network.
The ways that nations frame climate change (from newspaper content analysis) comprises the global field of climate change discourse. Within each nation, different sets of actors support different climate change framings. This project provides the basis for a proposed global real-time national climate change response monitoring and data bank system.
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We describe a simple and efficient method - the « Louvain method » - for the detection of communities in networks. The method has sub-quadratic computational complexity and can be routinely used for networks with billions of nodes or links ; the method was chosen a couple of months ago by LinkedIn for its network visualization tool. As an illustration of the method, we describe the communities obtained on a social network constructed from billions of mobile phone communications at country scale.

Online social media represent a fundamental shift of how information
is being produced, transferred and consumed. User generated content in
the form of blog posts, comments, and tweets establishes a connection
between the producers and the consumers of information. Tracking the
pulse of the social media outlets, enables companies to gain feedback
and insight in how to improve and market products better. For
consumers, the abundance of information and opinions from diverse

The importance of network analysis is growing across many domains, and is fundamental in understanding online social interactions, biological processes, communication, ecological, financial, transportation networks, and more. In most of these domains, the networks of interest are not directly observed, but must be inferred from noisy and incomplete data, data that was often generated for purposes other than scientific analysis. In this talk, I will introduce the problem of graph identification, the process of inferring the hidden network from noisy observational data.

Although the continuous circulation of information, news, jokes,
and opinions is ubiquitous in the worldwide social network, the
actual mechanics of how any single piece of information spreads
on a global scale have largely remained mysterious. A major
challenge lies in the difficulty of acquisition of large-scale
data recording the diffusion of any particular single piece of
information. This talk will focus on recent work tracing such
processes through online traces of information spreading. I will