Videos tagged with Network Analysis

Man-made or naturally-formed networks typically exhibit a high degree of structural regularity. In this paper, we introduce the problem of structured network prediction: given a set of n entities and a desired distribution for connectivity, return a likely set of edges connecting the entities together in a network having the specified degree distribution. Prediction is useful for initializing a...

The growth of Web 2.0 and fundamental theoretical breakthroughs have led to an avalanche of interest in social networks. This paper focuses on the problem of modeling how social networks accomplish tasks through peer production style collaboration. We propose a general interaction model for the underlying social networks and then a specific model (iLink) for social search and message routing. A...

The shift of communication to the internet, in particular to email, weblogs (blogs), and online communities, presents an opportunity to study the information dynamics of social networks on a large scale. Blogs, now numbering in the millions, are web pages updated using blogging software that makes it easy for authors to share new content online in the form of time-stamped posts. One can track h...

Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles - vertices that bridge clusters (hubs) ...

Diffusion is a process by which information, viruses, ideas and new behavior spread over the network. For example, adoption of a new technology begins on a small scale with a few “early adopters”, then more and more people adopt it as they observe friends and neighbors using it. Eventually the adoption of the technology may spread through the social network as an epidemic “inf...

A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been done toward modeling the dynamic processes underlying rewiring netwo...

Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, co...

Lecture slides: Emergent Networks as Distributed Reputation System Themes, conjectures Ripple Project BBC News - File sharing network Harvard internet TV New Group Selection Models Group Selection Models Evolutionary Group Selection Models SLAC: Network re-wire P2P model SLAC algorithm SLAC playing the PD SLAC and SLACER SLAC to SLACER SLACER – Some Results Experimental results with peopl...

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the ge...

We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as “unusually densely knit” subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing commun...