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Aristides Gionis
Researcher at Royal Institute of Technology
Publications - 316
Citations - 21244
Aristides Gionis is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Approximation algorithm & Graph (abstract data type). The author has an hindex of 58, co-authored 292 publications receiving 19300 citations. Previous affiliations of Aristides Gionis include Yahoo! & Aalto University.
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Proceedings ArticleDOI
Discovering Interesting Cycles in Directed Graphs
TL;DR: This paper addresses the problem of quantifying the extent to which a given cycle is interesting for a particular analyst and proposes a number of efficient heuristic algorithms that can be used to find interesting cycles in graphs.
Journal ArticleDOI
The network-untangling problem: from interactions to activity timelines
Polina Rozenshtein,Polina Rozenshtein,Nikolaj Tatti,Nikolaj Tatti,Aristides Gionis,Aristides Gionis +5 more
TL;DR: This paper provides two formulations of the network-untangling problem: (i) reducing the total interval length over all entities ( sum version), and (ii)minimizing the maximum interval length ( max version) and proposes efficient algorithms based on alternative optimization.
Posted Content
Finding path motifs in large temporal graphs using algebraic fingerprints
TL;DR: This work establishes complexity results and designs an algebraic-algorithmic framework based on constrained multilinear sieving for pattern-detection problems in vertex-colored temporal graphs, and demonstrates that the solution scales to massive graphs with up to a billion edges for a multiset query with 5 colors.
Proceedings ArticleDOI
Mining Signed Networks: Theory and Applications: Tutorial proposal for the Web Conference 2020
TL;DR: This tutorial aims to provide an overview of the literature in mining signed networks, the most important theoretical results since their inception to the present day, some of the most common applications, and to reflect on emerging applications and directions for future work.
Proceedings Article
Structured Prediction of Network Response
TL;DR: This work presents an approximate inference method through a semi-definite programming relaxation (SDP), as well as a more scalable greedy heuristic algorithm, called SPIN, that takes advantage of the context given by the actions and the network structure to improve predictive performance.