• Publications
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The community-search problem and how to plan a successful cocktail party
A lot of research in graph mining has been devoted in the discovery of communities. Most of the work has focused in the scenario where communities need to be discovered with only reference to theExpand
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Fast Hare: A Fast Heuristic for Single Individual SNP Haplotype Reconstruction
We study the single individual SNP haplotype reconstruction problem. We introduce a simple heuristic and prove experimentally that is very fast and accurate. In particular, when compared with aExpand
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SOFIE: a self-organizing framework for information extraction
This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE usesExpand
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STAR: Steiner-Tree Approximation in Relationship Graphs
Large graphs and networks are abundant in modern information systems: entity-relationship graphs over relational data or Web-extracted entities, biological networks, social online communities,Expand
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Efficient Densest Subgraph Computation in Evolving Graphs
Densest subgraph computation has emerged as an important primitive in a wide range of data analysis tasks such as community and event detection. Social media such as Facebook and Twitter are highlyExpand
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Finding Subgraphs with Maximum Total Density and Limited Overlap
Finding dense subgraphs in large graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most suchExpand
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Bridging the Terminology Gap in Web Archive Search
Web archives play an important role in preserving our cultural heritage for future generations. When searching them, a serious problem arises from the fact that terminology evolves constantly. SinceExpand
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Listing k-cliques in Sparse Real-World Graphs*
Motivated by recent studies in the data mining community which require to efficiently list all k-cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficientExpand
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Fully Dynamic k-Center Clustering
Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have beenExpand
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Finding near neighbors through cluster pruning
Finding near(est) neighbors is a classic, difficult problem in data management and retrieval, with applications in text and image search,in finding similar objects and matching patterns. Here weExpand
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