Huanhuan Wu

Large-scale distributed graph computing systems: an experimental evaluation

With the prevalence of graph data in real-world applications (e.g., social networks, mobile phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph computing systems have been developed in recent years to process and analyze massive graphs. Most of these systems adopt Pregels vertex-centric computing model, while various techniques have been proposed to address the limitations in the Pregel framework. However, there is a lack of comprehensive comparative analysis to evaluate the performance of various systems and their techniques, making it difficult for users to choose the best system for their applications. We conduct extensive experiments to evaluate the performance of existing systems on graphs with different characteristics and on algorithms with different design logic. We also study the effectiveness of various techniques adopted in existing systems, and the scalability of the systems. The results of our study reveal the strengths and limitations of existing systems, and provide valuable insights for users, researchers and system developers.

TF-Label: a topological-folding labeling scheme for reachability querying in a large graph

Reachability querying is a basic graph operation with numerous important applications in databases, network analysis, computational biology, software engineering, etc. Although many indexes have been proposed to answer reachability queries, most of them are only efficient for handling relatively small graphs. We propose TF-label, an efficient and scalable labeling scheme for processing reachability queries. TF-label is constructed based on a novel topological folding (TF) that recursively folds an input graph into half so as to reduce the label size, thus improving query efficiency. We show that TF-label is efficient to construct and propose efficient algorithms and optimization schemes. Our experiments verify that TF-label is significantly more scalable and efficient than the state-of-the-art methods in both index construction and query processing.

IS-Label: an independent-set based labeling scheme for point-to-point distance querying

We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 million vertices, which is rather small in view of many of the fast-growing real-world graphs today such as social networks and Web graphs. We propose an efficient index, which is a novel labeling scheme based on the independent set of a graph. We show that our method can handle graphs of size three orders of magnitude larger than those existing indexes.

Core decomposition in large temporal graphs

Core decomposition has been applied widely in the visualization and analysis of massive networks. However, existing studies of core decomposition were only limited to non-temporal graphs, while many real-world graphs can be naturally modeled as temporal graphs (e.g., the interaction between users at different time in online social networks, the phone call or messaging records between friends over time, etc.). In this paper, we define the problem of core decomposition in a temporal graph, propose efficient distributed algorithms to compute the cores in massive temporal graphs, and discuss how the technique can be used in temporal graph analysis.

Reachability and time-based path queries in temporal graphs

A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., A calls B at 11 a.m. and talks for 7 minutes, which is modeled by an edge from A to B with starting time “11 a.m.” and duration “7 mins”. Temporal graphs can be used to model many networks with time-related activities, but efficient algorithms for analyzing temporal graphs are severely inadequate. We study fundamental problems such as answering reachability and time-based path queries in a temporal graph, and propose an efficient indexing technique specifically designed for processing these queries in a temporal graph. Our results show that our method is efficient and scalable in both index construction and query processing.

Efficient Algorithms for Temporal Path Computation

Shortest path is a fundamental graph problem with numerous applications. However, the concept of classic shortest path is insufficient. In this paper, we study various concepts of “shortest” path in temporal graphs, called minimum temporal paths. Computing these minimum temporal paths is challenging as subpaths of a “shortest” path may not be “shortest” in a temporal graph. We propose efficient algorithms to compute minimum temporal paths and verified their efficiency using large real-world temporal graphs.

Diversified Temporal Subgraph Pattern Mining

Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs.

Efficient Processing of Growing Temporal Graphs

Temporal graphs are useful in modeling real-world networks. For example, in a phone call network, people may communicate with each other in multiple time periods, which can be modeled as multiple temporal edges. However, the size of real-world temporal graphs keeps increasing rapidly (e.g., considering the number of phone calls recorded each day), which makes it difficult to efficiently store and analyze the complete temporal graphs. We propose a new model, called equal-weight damped time window model, to efficiently manage temporal graphs. In this model, each time window is assigned a unified weight. This model is flexible as it allows users to control the tradeoff between the required storage space and the information loss. It also supports efficient maintenance of the windows as new data come in. We then discuss applications that use the model for analyzing temporal graphs. Our experiments demonstrated that we can handle massive temporal graphs efficiently with limited space.