Uncertain Graph Sparsification

• Published in: IEEE transactions on knowledge and data engineering Vol. 30; no. 12; pp. 2435 - 2449
• Main Authors: Parchas, Panos, Papailiou, Nikolaos, Papadias, Dimitris, Bonchi, Francesco
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; data graph algorithms; data structures; database management; discrete mathematics; Electronic mail; Entropy; fuzzy and probabilistic reasoning; Graph theory; Graphs; graphs and networks; information interfaces and representation (HCI); information technology and systems; mathematics of computing; Nickel; Query processing; Reliability; Shortest-path problems; Social network services; Task analysis; Uncertainty; user Interfaces
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2018.2819651

Uncertain graphs are prevalent in several applications including communications systems, biological databases, and social networks. The ever increasing size of the underlying data renders both graph storage and query processing extremely expensive. Sparsification has often been used to reduce the size of deterministic graphs by maintaining only the important edges. However, adaptation of deterministic sparsification methods fails in the uncertain setting. To overcome this problem, we introduce the first sparsification techniques aimed explicitly at uncertain graphs. The proposed methods reduce the number of edges and redistribute their probabilities in order to decrease the graph size, while preserving its underlying structure. The resulting graph can be used to efficiently and accurately approximate any query and mining tasks on the original graph. An extensive experimental evaluation with real and synthetic datasets illustrates the effectiveness of our techniques on several common graph tasks, including clustering coefficient, page rank, reliability, and shortest path distance.