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Found 6 papers, 4 papers with code
BBK: a simpler, faster algorithm for enumerating maximal bicliques in large sparse bipartite graphs
We also provide an open-access implementation of BBK in C++, which we use to experiment and validate its efficiency on massive real-world datasets and show that its execution time is shorter in practice than state-of-the art algorithms.
LSCPM: communities in massive real-world Link Streams by Clique Percolation Method
We present a novel algorithm that adapts CPM to link streams, which has the advantage that it allows us to speed up the computation time with respect to the existing DCPM method.
Faster maximal clique enumeration in large real-world link streams
We take this idea as a starting point to propose a new algorithm which matches the cliques of the instantaneous graphs formed by links existing at a given time $t$ to the maximal cliques of the link stream.
Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity
We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations, then investigate if these effects are only due to the semantic content encoded in the network.
Measuring Diversity in Heterogeneous Information Networks
In this article, we develop a formal framework for the application of a large family of diversity measures to heterogeneous information networks (HINs), a flexible, widely-used network data formalism.
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns.
