Multimodal graph-based analysis over the DBLP repository

Abstract

The use of graph theory for analyzing network-like data has gained central importance with the rise of the Web 2.0. However, many graph-based techniques are not welldisseminated and neither explored at their full potential, what might depend on a complimentary approach achieved with the combination of multiple techniques. This paper describes the systematic use of graph-based techniques of different types (multimodal) combining the resultant analytical insights around a common domain, the Digital Bibliography & Library Project (DBLP). To do so, we introduce an analytical ensemble based on statistical (degree, and weakly-connected components distribution), topological (average clustering coefficient, and effective diameter evolution), algorithmic (link prediction/machine learning), and algebraic techniques to inspect non-evident features of DBLP at the same time that we interpret the heterogeneous discoveries found along the work. As a result, we have put together a set of techniques demonstrating over DBLP what we call multimodal analysis, an innovative process of information understanding that demands a wide technical knowledge and a deep understanding of the data domain. We expect that our methodology and our findings will foster other multimodal analyses and also that they will bring light over the Computer Science research.

Enabling application‐aware flexible graph partition mechanism for parallel graph processing systems

“…Typically, the graph can be used to model the references among literal papers [1,2], the acquaintances in social networks [3][4][5][6], and the hyper links linking bunches of web pages [7][8][9][10]. Typically, the graph can be used to model the references among literal papers [1,2], the acquaintances in social networks [3][4][5][6], and the hyper links linking bunches of web pages [7][8][9][10].…”

Block-based and structure-based techniques for large-scale graph processing and visualization

“…Blocks in M-Flash: Given a graph G, we divide its vertices V into β intervals denoted by I (p) , G (3,3) Source I (2) Source I (1) Source I (3) Destination I (3) Destination I (2) Destination I (1) G (2,3) G (1,3) G (3,2) G (2,2) G (1,2) G (3,1) G (2,1) G (1,1) Figure 9 -M-Flash's computation schedule for a graph with 3 intervals. Vertex intervals are represented by vertical (Source I) and horizontal (Destination I) vectors.…”

“…Each block G (p,q) has a source node interval p and a destination node interval q, where 1 ≤ p, q ≤ β . In Figure 8, for example, G (2,1) is the block that contains edges with source vertices in the interval I (2) and destination vertices in the interval I (1) . In total, we have β 2 blocks.…”

“…Dense Block Processing (DBP): Figure 9 illustrates the DBP processing; notice that vertex intervals are represented by vertical (Source I) and horizontal (Destination I) vectors. After Figure 10 -Example I/O operations to process the dense block G (2,1) .…”

“…Blocks are loaded into memory, and processed, in a vertical zigzag manner, indicated by the sequence of red, orange and yellow arrows. This enables the reuse of input (e.g., when going from G (3,1) to G (3,2) , M-Flash reuses source node interval I (3) ), which reduces data transfer from disk to memory, Figure -Example I/O operations to process the dense block G (2,1) . .…”