230 research outputs found
Cohesive Subgraph Detection in Massive Networks
Due to the strong expressive power of the graph model, many real-world applications model data and relationships among the data as a graph, and significant research efforts have been devoted towards efficiently and effectively managing and analyzing graph data. Among them, mining and querying cohesive subgraph structure in massive networks is of great importance for a deeper understanding and better management of such networks. However, the massive graph volume and rapid evolution present huge challenges, which need highly efficient solutions. In this thesis, we study three important problems in mining cohesive subgraph structure in massive networks, and designs efficient and scalable solutions.
Firstly, We study the problem of structural graph clustering. We develop a new two-step paradigm for scalable structural graph clustering based on our three new observations. Then, we present a pSCAN approach, and propose optimization techniques to speed up checking whether two vertices are structure-similar. Moreover, we also propose efficient techniques for updating the clusters when the input graph dynamically changes.
Secondly, we formulate and investigate the problem of diversified top-k community detection over labeled graphs. We introduce a model, called special-interest-group, to enforce both structural cohesiveness and focused interests of a community. We prove that computing the top-1 community is NP-hard. Nevertheless, we propose effective pruning techniques to efficiently enumerate all communities in a graph, based on which we then select diversified top-k communities in a greedy manner. We prove that our algorithm computes the top-k communities approximately but with a guaranteed approximation ratio.
Finally, we study the problem of efficiently computing a maximum independent set from a large graph G (a maximum clique in the complement graph of G). We develop a Reducing-Peeling framework which iteratively reduces the graph size by applying reduction rules on vertices with very low degrees (Reducing) and temporarily removing with the highest degree (Peeling) if the reduction rules cannot be applied. Secondly, based on our framework we design two baseline algorithms, a linear-time algorithm and a near-linear time algorithm, by designing new reduction rules and developing techniques for efficiently and incrementally applying reduction rules
Exploring Communities in Large Profiled Graphs
Given a graph and a vertex , the community search (CS) problem
aims to efficiently find a subgraph of whose vertices are closely related
to . Communities are prevalent in social and biological networks, and can be
used in product advertisement and social event recommendation. In this paper,
we study profiled community search (PCS), where CS is performed on a profiled
graph. This is a graph in which each vertex has labels arranged in a
hierarchical manner. Extensive experiments show that PCS can identify
communities with themes that are common to their vertices, and is more
effective than existing CS approaches. As a naive solution for PCS is highly
expensive, we have also developed a tree index, which facilitate efficient and
online solutions for PCS
Graph pattern matching on social network analysis
Graph pattern matching is fundamental to social network analysis. Its effectiveness
for identifying social communities and social positions, making recommendations and
so on has been repeatedly demonstrated. However, the social network analysis raises
new challenges to graph pattern matching. As real-life social graphs are typically
large, it is often prohibitively expensive to conduct graph pattern matching over such
large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded
simulation, and quadratic time for simulation. These hinder the applicability of graph
pattern matching on social network analysis. In response to these challenges, the thesis
presents a series of effective techniques for querying large, dynamic, and distributively
stored social networks.
First of all, we propose a notion of query preserving graph compression, to compress
large social graphs relative to a class Q of queries. We then develop both batch
and incremental compression strategies for two commonly used pattern queries. Via
both theoretical analysis and experimental studies, we show that (1) using compressed
graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr
as well as its maintenance can be processed efficiently.
Secondly, we investigate the distributed graph pattern matching problem, and explore
parallel computation for graph pattern matching. We show that our techniques
possess following performance guarantees: (1) each site is visited only once; (2) the total
network traffic is independent of the size of G; and (3) the response time is decided
by the size of largest fragment of G rather than the size of entire G. Furthermore, we
show how these distributed algorithms can be implemented in the MapReduce framework.
Thirdly, we study the problem of answering graph pattern matching using views
since view based techniques have proven an effective technique for speeding up query
evaluation. We propose a notion of pattern containment to characterise graph pattern
matching using views, and introduce efficient algorithms to answer graph pattern
matching using views. Moreover, we identify three problems related to graph pattern
containment, and provide efficient algorithms for containment checking (approximation
when the problem is intractable).
Fourthly, we revise graph pattern matching by supporting a designated output node,
which we treat as āquery focusā. We then introduce algorithms for computing the top-k
relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively,
with early termination property. Furthermore, we investigate the diversified
top-k matching problem, and develop an approximation algorithm with performance
guarantee and a heuristic algorithm with early termination property.
Finally, we introduce an expert search system, called ExpFinder, for large and dynamic
social networks. ExpFinder identifies top-k experts in social networks by graph
pattern matching, and copes with the sheer size of real-life social networks by integrating
incremental graph pattern matching, query preserving compression and top-k
matching computation. In particular, we also introduce bounded (resp. unbounded)
incremental algorithms to maintain the weighted landmark vectors which are used for
incremental maintenance for cached results
Considering User Intention in Differential Graph Queries
Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on usersā preferences and significantly support the usersā understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph
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