Jiafeng Hu

Effective community search over large spatial graphs

Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS) has received plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing CS solutions do not consider the spatial extent of a community. They can yield communities whose locations of vertices span large areas. In applications that facilitate the creation of social events (e.g., finding conference attendees to join a dinner), it is important to find groups of people who are physically close to each other. In this situation, it is desirable to have a spatial-aware community (or SAC), whose vertices are close structurally and spatially. Given a graph G and a query vertex q, we develop exact solutions for finding an SAC that contains q. Since these solutions cannot scale to large datasets, we have further designed three approximation algorithms to compute an SAC. We have performed an experimental evaluation for these solutions on both large real and synthetic datasets. Experimental results show that SAC is better than the communities returned by existing solutions. Moreover, our approximation solutions can find SACs accurately and efficiently.

Querying Minimal Steiner Maximum-Connected Subgraphs in Large Graphs

Given a graph G and a set Q of query nodes, we examine the Steiner Maximum-Connected Subgraph (SMCS). The SMCS, or Gs induced subgraph that contains Q with the largest connectivity, can be useful for customer prediction, product promotion, and team assembling. Despite its importance, the SMCS problem has only been recently studied. Existing solutions evaluate the maximum SMCS, whose number of nodes is the largest among all the SMCSs of Q. However, the maximum SMCS, which may contain a lot of nodes, can be difficult to interpret. In this paper, we investigate the minimal SMCS, which is the minimal subgraph of G with the maximum connectivity containing Q. The minimal SMCS contains much fewer nodes than its maximum counterpart, and is thus easier to be understood. However, the minimal SMCS can be costly to evaluate. We thus propose efficient Expand-Refine algorithms, as well as their approximate versions with accuracy guarantees. Extensive experiments on six large real graph datasets validate the effectiveness and efficiency of our approaches.

C-explorer: browsing communities in large graphs

Community retrieval (CR) algorithms, which enable the extraction of subgraphs from large social networks (e.g., Facebook and Twitter), have attracted tremendous interest. Various CR solutions, such as k-core and codicil, have been proposed to obtain graphs whose vertices are closely related. In this paper, we propose the C-Explorer system to assist users in extracting, visualizing, and analyzing communities. C-Explorer provides online and interactive CR facilities, allowing a user to view her interesting graphs, indicate her required vertex q, and display the communities to which q belongs. A seminal feature of C-Explorer is that it uses an attributed graph, whose vertices are associated with labels and keywords, and looks for an attributed community (or AC), whose vertices are structurally and semantically related. Moreover, C-Explorer implements several state-of-the-art CR algorithms, as well as functions for analyzing their effectiveness. We plan to make C-Explorer an open-source web-based platform, and design API functions for software developers to test their CR algorithms in our system.

On Embedding Uncertain Graphs

Graph data are prevalent in communication networks, social media, and biological networks. These data, which are often noisy or inexact, can be represented by uncertain graphs, whose edges are associated with probabilities to indicate the chances that they exist. Recently, researchers have studied various algorithms (e.g., clustering, classification, and k-NN) for uncertain graphs. These solutions face two problems: (1) high dimensionality: uncertain graphs are often highly complex, which can affect the mining quality; and (2) low reusability, where an existing mining algorithm has to be redesigned to deal with uncertain graphs. To tackle these problems, we propose a solution called URGE, or UnceRtain Graph Embedding. Given an uncertain graph G, URGE generates Gs embedding, or a set of low-dimensional vectors, which carry the proximity information of nodes in G. This embedding enables the dimensionality of G to be reduced, without destroying node proximity information. Due to its simplicity, existing mining solutions can be used on the embedding. We investigate two low- and high-order node proximity measures in the embedding generation process, and develop novel algorithms to enable fast evaluation. To our best knowledge, there is no prior study on the use of embedding for uncertain graphs. We have further performed extensive experiments for clustering, classification, and k-NN on several uncertain graph datasets. Our results show that URGE attains better effectiveness than current uncertain data mining algorithms, as well as state-of-the-art embedding solutions. The embedding and mining performance is also highly efficient in our experiments.

Efficient Top-k Subscription Matching for Location-Aware Publish/Subscribe

The dissemination of messages to a vast number of mobile users has raised a lot of attention. This issue is inherent in emerging applications, such as location-based targeted advertising, selective information disseminating, and ride sharing. In this paper, we examine how to support location-based message dissemination in an effective and efficient manner. Our main idea is to develop a location-aware version of the Pub/Sub model, which was designed for message dissemination. While a lot of studies have successfully used this model to match the interest of subscriptions (e.g., the properties of potential customers) and events (e.g., information of casual users), the issues of incorporating the location information of subscribers and publishers have not been well addressed. We propose to model subscriptions and events by boolean expressions and location data. This allows complex information to be specified. However, since the number of publishers and subscribers can be enormous, the time cost for matching subscriptions and events can be prohibitive. To address this problem, we have developed the \(R^I\)-tree. This data structure is an integration of the R-tree and the dynamic interval-tree. Together with our novel pruning strategy on \(R^I\)-tree, our solution can effectively and efficiently return the top-k subscriptions with respect to an event. We have performed extensive evaluations to verify our approach.

Effective and efficient attributed community search

Given a graph G and a vertex

Effective community search for large attributed graphs

q \in G

On Minimal Steiner Maximum-Connected Subgraph Queries

q∈G, the community search query returns a subgraph of G that contains vertices related to q. Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and setting up of social events. In this paper, we investigate the attributed community query (or ACQ), which returns an attributed community (AC) for an attributed graph. The AC is a subgraph of G, which satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). The AC enables a better understanding of how and why a community is formed (e.g., members of an AC have a common interest in music, because they all have the same keyword “music”). An AC can be “personalized”; for example, an ACQ user may specify that an AC returned should be related to some specific keywords like “research” and “sports”. To enable efficient AC search, we develop the CL-tree index structure and three algorithms based on it. We further propose efficient algorithms for maintaining the index on dynamic graphs. Moreover, we study two problems that are related to the ACQ problem. We evaluate our solutions on six large graphs. Our results show that ACQ is more effective and efficient than existing community retrieval approaches. Moreover, an AC contains more precise and personalized information than that of existing community search and detection methods.