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Graphs have been widely used in many applications such as social networks, collaboration networks, and biological networks. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, k-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance...
A hypergraph allows a hyperedge to connect more than two vertices, using which to capture the high-order relationships, many hypergraph learning algorithms are shown highly effective in various applications. When learning large hypergraphs, converting them to graphs to employ the distributed graph frameworks is a common approach, yet it results in major efficiency drawbacks including an inflated problem...
We introduce and formulate two types of random-walk domination problems in graphs motivated by a number of applications in practice (e.g., item-placement problem in online social networks, Ads-placement problem in advertisement networks, and resource-placement problem in P2P networks). Specifically, given a graph G, the goal of the first type of random-walk domination problem is to target k nodes...
As an important branch of big data processing, big graph processing is becoming increasingly popular in recent years. Strongly connected component (SCC) computation is a fundamental graph operation on directed graphs, where an SCC is a maximal subgraph S of a directed graph G in which every pair of nodes is reachable from each other in S. By contracting each SCC into a node, a large general directed...
Graph has been widely used as a data structure to abstract complex relationships among entities in a form on which algorithms are designed and systems are developed to maintain information, understand the complex relationships, and discovery knowledge. In this talk, we explore several research issues over large graphs.We introduce some research problems to be discussed: large graphs matching, graph...
Search for objects similar to a given query object in a network has numerous applications including web search and collaborative filtering. We use the notion of structural similarity to capture the commonality of two objects in a network, e.g., if two nodes are referenced by the same node, they may be similar. Meeting-based methods including SimRank and P-Rank capture structural similarity very well...
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