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Found 7 papers, 1 papers with code
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs
Our algorithm is based on a new approach to computing $(1+\epsilon)$-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of $(1+\epsilon)$-approximate HAC.
Phase transitions in the Prisoner's Dilemma game on scale-free networks
We study stochastic dynamics of the Prisoner's Dilemma game on random Erd\"{o}s-R\'{e}nyi and Barab\'{a}si-Albert networks with a cost of maintaining a link between interacting players.
Constant Approximation for Normalized Modularity and Associations Clustering
We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster.
Scalable Community Detection via Parallel Correlation Clustering
Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection.
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
For this variant, this is the first exact algorithm that runs in subquadratic time, as long as $m=n^{2-\epsilon}$ for some constant $\epsilon > 0$.
Faster DBSCAN via subsampled similarity queries
DBSCAN is a popular density-based clustering algorithm.
Community Detection on Evolving Graphs
In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph.
