Joint direction of arrival (DOA) and Doppler frequency estimation plays an important role in radar and wireless communication systems. However, the number of antennas and snapshots could be limited in real applications, so that it is necessary to design the array structure and sampling method to improve the estimation performance. We utilize coprime array and coprime sampler at the spatial and temporal domains under a constrained number of total snapshots. We propose to allow different temporal sampling parameters for the coprime samplers at different antennas, called STCS (Spatial-Temporal Coprime Sampling with possibly different temporal sampling parameters). The spatial coprime array and temporal coprime sampler parameters are optimized to maximize the degrees of freedom (DOFs). Based on the optimized coprime array and samplers, compressed sensing theory is used to jointly estimate the DOA and Doppler frequency. Numerical examples are presented to illustrate that the proposed STCS can provide the improved DOF and better estimation performance.

Item cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having interaction record) and that on cold-start items using the Wasserstein distance. On this basis, we propose Wasserstein Collaborative Filtering (WCF), which predicts user preference on cold-start items by minimizing the Wasserstein distance under user embedding constraint. Our analysis shows that minimizing the Wasserstein distance ensures that users sharing similar tastes on warm items also have similar preferences on cold-start items. Experimental results show that WCF consistently outperform the state-of-the-art methods in recommendation quality, usually by a large margin.

We propose GraphD, an out-of-core Pregel-like system targeting efficient big graph processing with a small cluster of commodity PCs connected by Gigabit Ethernet, an environment affordable to most users. This is in contrast to some recent efforts for out-of-core graph computation with specialized hardware. In our setting, a vertex-centric program is often data-intensive, since the CPU cost of calculating a message value is negligible compared with the network cost of transmitting that message. As a result, network bandwidth is usually the bottleneck, and out-of-core execution would not sacrifice performance if disk IO overhead can be hidden by message transmission, which is achieved by GraphD through the parallelism of computation and communication. GraphD streams edge and message data on local disks, and thus consumes negligible memory space. For a broad class of Pregel algorithms where message combiner is applicable, GraphD completely eliminates the need of any expensive external-memory join or group-by, which is required by existing systems such as Pregelix and Chaos. Extensive experiments show that GraphD beats existing out-of-core systems by orders of magnitude, and achieves comparable performance to in-memory systems running with adequate memory.

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.

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.

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.

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.

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 Pregel's 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.

Shortest path is a fundamental graph problem with numerous applications. However, the concept of classic shortest path is insufficient or even flawed in a temporal graph, as the temporal information determines the order of activities along any path. In this paper, we show the shortcomings of classic shortest path in a temporal graph, and study various concepts of "shortest" path for temporal graphs. Computing these temporal paths is challenging as subpaths of a "shortest" path may not be "shortest" in a temporal graph. We investigate properties of the temporal paths and propose efficient algorithms to compute them. We tested our algorithms on real world temporal graphs to verify their efficiency, and also show that temporal paths are essential for studying temporal graphs by comparing shortest paths in normal static graphs.

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.

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 orders of magnitude larger than existing indexes.