Multi-Relational Characterization of Dynamic Social Network Communities

  title={Multi-Relational Characterization of Dynamic Social Network Communities},
  author={Y. Lin and H. Sundaram and Aisling Kelliher},
  booktitle={Handbook of Social Network Technologies},
The emergence of the mediated social web – a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies – has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of “community”. The growth of online communities offers both opportunities and… 
1 Citations

Probabilistic inference for dynamic networks and complex event processes

A significant contribution of this thesis is an efficient Bayesian inference algorithm for Gaussian process modulated Poisson processes that scales linearly in the number of observed events and does not require discretisation of the input domain, or expensive Monte-Carlo simulation, that previous posterior inference algorithms for this model required.



Group formation in large social networks: membership, growth, and evolution

It is found that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure, and decision-tree techniques are used to identify the most significant structural determinants of these properties.

Preferential behavior in online groups

It is shown that users who will go on to become long-lived, highly-engaged users experience significantly better treatment than other users from the moment they join the group, well before there is an opportunity for them to develop a long-standing relationship with members of the group.

Quantifying social group evolution

The focus is on networks capturing the collaboration between scientists and the calls between mobile phone users, and it is found that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability.

Analyzing communities and their evolutions in dynamic social networks

This paper proposes FacetNet, a novel framework for analyzing communities and their evolutions through a robust unified process, and develops an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution.

Facetnet: a framework for analyzing communities and their evolutions in dynamic networks

This paper proposes FacetNet, a novel framework for analyzing communities and their evolutions through a robust unified process, where communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions.

The Strength of Weak Ties

Analysis of social networks is suggested as a tool for linking micro and macro levels of sociological theory. The procedure is illustrated by elaboration of the macro implications of one aspect of

Discovery of Blog Communities based on Mutual Awareness

This work uses the mutual awareness feature with a rankingbased community extraction algorithm to discover communities and extracts communities that demonstrate to be semantically cohesive with respect to their topics of interest.

MetaFac: community discovery via relational hypergraph factorization

The proposed MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions, outperform baseline methods by an order of magnitude and is able to extract meaningful communities based on the social media contexts.

Social Network Analysis: Methods and Applications

This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of dyadic and triadic interaction models, which provide insights into the structure and dynamics of relationships between actors and groups.

Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

This work proposes to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation.