In this paper, we leverage the concept of the metric backbone to improve the efficiency of large-scale graph analytics. The metric backbone is the minimum subgraph that preserves the shortest paths of a weighted graph. We use the metric backbone in place of the original graph to compute various graph metrics exactly or with good approximation. By computing on a smaller graph, we improve the performance of graph analytics applications on two different systems, a batch graph processing system and a graph database. Further, we provide an algorithm for the computation of the metric backbone on large graphs. While one can compute the metric backbone by solving the all-pairs-shortest-paths problem, this approach incurs prohibitive time and space complexity for big graphs. Instead, we propose a heuristic that makes computing the metric backbone practical even for large graphs. Additionally, we analyze several real datasets of different sizes and domains and we show that we can approximate the metric backbone by removing only first-order semi-metric edges; edges for which a shorter two-hop path exists. We provide a distributed implementation of our algorithm and apply it in large scale scenarios. We evaluate our algorithm using a variety of real graphs, including a Facebook social network subgraph of ~50 billion edges. We measure the impact of using the metric backbone on runtime performance in two graph management systems. We achieve query speedups of up to 6.7x in the Neo4j commercial graph database and job speedups of up to 6x in the Giraph graph processing system.

This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.

In this study, we crawled a local Web domain, created its graph representation, and analyzed the network structure. The results of network analysis revealed local scale-free patterns consistent with previous research on the Web. To discover topical communities of the local domain, link- based co-citation analysis was performed to measure pair-wise similarities. Based on the co-citation graph, a visualization was produced to map the networked domain semantically. All of this offers a way to investigate a local Web structure and to make it more intuitively understandable.

We describe a network approach to building recommendation systems for a Web service. We employ two different types of weighted graphs in our analysis and development: proximity graphs, a type of fuzzy graphs based on a co-occurrence probability, and semi-metric distance graphs, which do not observe the triangle inequality of Euclidean distances. Both types of graphs are used to develop intelligent recommendation and collaboration systems for the MyLibrary@LANL Web service, a user-centered front-end to the Los Alamos National Laboratory's digital library collections and Web resources.

BACKGROUND: We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a protein's document neighborhood into the GO. RESULTS: The evaluation results indicate that the method for expanding words associated with GO nodes is quite powerful; we were able to successfully select appropriate evidence text for a given annotation in 38% of Task 2.1 queries by building on this method. The term categorization methodology achieved a precision of 16% for annotation within the correct extended family in Task 2.2, though we show through subsequent analysis that this can be improved with a different parameter setting. Our architecture proved not to be very successful on the evidence text component of the task, in the configuration used to generate the submitted results. CONCLUSION: The initial results show promise for both of the methods we explored, and we are planning to integrate the methods more closely to achieve better results overall.