arXiv subjects

Xifeng Yan

Publications and source records attributed to Xifeng Yan.

58 records · Page 4Linked to original sources

arXiv2012

MaTrust: An Effective Multi-Aspect Trust Inference Model

Trust is a fundamental concept in many real-world applications such as e-commerce and peer-to-peer networks. In these applications, users can generate local opinions about the counterparts based on direct experiences, and these opinions can then be aggregated to build trust among unknown users. The mechanism to build new trust relationships based on existing ones is referred to as trust inference. State-of-the-art trust inference approaches employ the transitivity property of trust by propagating trust along connected users. In this paper, we propose a novel trust inference model (MaTrust) by exploring an equally important property of trust, i.e., the multi-aspect property. MaTrust directly characterizes multiple latent factors for each trustor and trustee from the locally-generated trust relationships. Furthermore, it can naturally incorporate prior knowledge as specified factors. These factors in turn serve as the basis to infer the unseen trustworthiness scores. Experimental evaluations on real data sets show that the proposed MaTrust significantly outperforms several benchmark trust inference models in both effectiveness and efficiency.

cs.DB↗

arXiv2012

Inferring the Underlying Structure of Information Cascades

In social networks, information and influence diffuse among users as cascades. While the importance of studying cascades has been recognized in various applications, it is difficult to observe the complete structure of cascades in practice. Moreover, much less is known on how to infer cascades based on partial observations. In this paper we study the cascade inference problem following the independent cascade model, and provide a full treatment from complexity to algorithms: (a) We propose the idea of consistent trees as the inferred structures for cascades; these trees connect source nodes and observed nodes with paths satisfying the constraints from the observed temporal information. (b) We introduce metrics to measure the likelihood of consistent trees as inferred cascades, as well as several optimization problems for finding them. (c) We show that the decision problems for consistent trees are in general NP-complete, and that the optimization problems are hard to approximate. (d) We provide approximation algorithms with performance guarantees on the quality of the inferred cascades, as well as heuristics. We experimentally verify the efficiency and effectiveness of our inference algorithms, using real and synthetic data.

cs.SI↗

arXiv2012

Measuring Two-Event Structural Correlations on Graphs

Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal important relationships between different events. Unfortunately, correlation relationships on graph structures are not well studied and cannot be captured by traditional measures. In this work, we design a novel measure for assessing two-event structural correlations on graphs. Given the occurrences of two events, we choose uniformly a sample of "reference nodes" from the vicinity of all event nodes and employ the Kendall's tau rank correlation measure to compute the average concordance of event density changes. Significance can be efficiently assessed by tau's nice property of being asymptotically normal under the null hypothesis. In order to compute the measure in large scale networks, we develop a scalable framework using different sampling strategies. The complexity of these strategies is analyzed. Experiments on real graph datasets with both synthetic and real events demonstrate that the proposed framework is not only efficacious, but also efficient and scalable.

cs.DB↗

arXiv2012

Memory Efficient De Bruijn Graph Construction

Massively parallel DNA sequencing technologies are revolutionizing genomics research. Billions of short reads generated at low costs can be assembled for reconstructing the whole genomes. Unfortunately, the large memory footprint of the existing de novo assembly algorithms makes it challenging to get the assembly done for higher eukaryotes like mammals. In this work, we investigate the memory issue of constructing de Bruijn graph, a core task in leading assembly algorithms, which often consumes several hundreds of gigabytes memory for large genomes. We propose a disk-based partition method, called Minimum Substring Partitioning (MSP), to complete the task using less than 10 gigabytes memory, without runtime slowdown. MSP breaks the short reads into multiple small disjoint partitions so that each partition can be loaded into memory, processed individually and later merged with others to form a de Bruijn graph. By leveraging the overlaps among the k-mers (substring of length k), MSP achieves astonishing compression ratio: The total size of partitions is reduced from $Θ(kn)$ to $Θ(n)$, where $n$ is the size of the short read database, and $k$ is the length of a $k$-mer. Experimental results show that our method can build de Bruijn graphs using a commodity computer for any large-volume sequence dataset.

cs.DS↗