Francesco Gullo

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Finding dense subgraphs is an important graph-mining task with many applications. Given that the direct optimization of edge density is not meaningful, as even a single edge achieves maximum density, research has focused on optimizing alternative density functions. A very popular among such functions is the average degree, whose maximization leads to the(More)
Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of(More)
Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probability of nodes being reachable one from another. Existing(More)
We study a novel clustering problem in which the pairwise relations between objects are <i>categorical</i>. This problem can be viewed as clustering the vertices of a graph whose edges are of different types (<i>colors</i>). We introduce an objective function that aims at partitioning the graph such that the edges within each cluster have, as much as(More)
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 such applications, one typically aims at finding several (possibly overlapping) dense subgraphs which might correspond to communities in social networks or(More)
Community search is the problem of finding a good community for a given set of query vertices. One of the most studied formulations of community search asks for a connected subgraph that contains all query vertices and maximizes the minimum degree. All existing approaches to min-degree-based community search suffer from limitations concerning efficiency, as(More)
A considerable amount of work has been done in data clustering research during the last four decades, and a myriad of methods has been proposed focusing on different data types, proximity functions, cluster representation models, and cluster presentation. However, clustering remains a challenging problem due to its ill-posed nature: it is well known that(More)