no code implementations • 27 Jan 2020 • Saket Gurukar, Deepak Ajwani, Sourav Dutta, Juho Lauri, Srinivasan Parthasarathy, Alessandra Sala
Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity
1 code implementation • 20 Jan 2020 • Suhas Thejaswi, Aristides Gionis, Juho Lauri
In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query.
no code implementations • 5 Jan 2020 • Juho Lauri, Sourav Dutta, Marco Grassia, Deepak Ajwani
For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques.
no code implementations • 12 Sep 2019 • Marco Grassia, Juho Lauri, Sourav Dutta, Deepak Ajwani
Compared to the state-of-the-art heuristics and preprocessing strategies, the advantages of our approach are that (i) it does not require any estimate on the maximum clique size at runtime and (ii) we demonstrate it to be effective also for dense graphs.
no code implementations • 22 Feb 2019 • Juho Lauri, Sourav Dutta
We propose a simple, powerful, and flexible machine learning framework for (i) reducing the search space of computationally difficult enumeration variants of subset problems and (ii) augmenting existing state-of-the-art solvers with informative cues arising from the input distribution.