Discovering social circles in ego networks
@article{McAuley2012DiscoveringSC, title={Discovering social circles in ego networks}, author={Julian McAuley and Jure Leskovec}, journal={ACM Transactions on Knowledge Discovery from Data (TKDD)}, year={2012}, volume={8}, pages={1 - 28} }
People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. [] Key Method We develop a model for detecting circles that combines network structure as well as user profile information. For each circle, we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately…
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References
SHOWING 1-10 OF 65 REFERENCES
Learning to Discover Social Circles in Ego Networks
- Computer ScienceNIPS
- 2012
A novel machine learning task of identifying users' social circles is defined as a node clustering problem on a user's ego-network, a network of connections between her friends, and a model for detecting circles is developed that combines network structure as well as user profile information.
You are who you know: inferring user profiles in online social networks
- Computer ScienceWSDM '10
- 2010
It is found that users with common attributes are more likely to be friends and often form dense communities, and a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks is proposed.
The Anatomy of the Facebook Social Graph
- Computer ScienceArXiv
- 2011
A strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality are observed, but it is shown that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure.
Community-Affiliation Graph Model for Overlapping Network Community Detection
- Computer Science2012 IEEE 12th International Conference on Data Mining
- 2012
The proposed Community-Affiliation Graph Model is a model-based community detection method that builds on bipartite node-community affiliation networks that successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.
Group formation in large social networks: membership, growth, and evolution
- Computer ScienceKDD '06
- 2006
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.
Empirical comparison of algorithms for network community detection
- Computer ScienceWWW '10
- 2010
Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.
Detecting community structure in networks
- Computer Science
- 2004
A number of more recent algorithms that appear to work well with real-world network data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks are described.
Model‐based clustering for social networks
- Computer Science
- 2007
A new model is proposed, the latent position cluster model, under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean ‘social space’, and the actors’ locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster.
Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.
- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2009
The basic ideas behind the previous benchmark are extended to generate directed and weighted networks with built-in community structure, and the possibility that nodes belong to more communities is considered, a feature occurring in real systems, such as social networks.
Birds of a Feather: Homophily in Social Networks
- Psychology
- 2001
Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange,…