Given a large graph with several millions or billions of nodes and edges, such as a social network, how can we
explore
it efficiently and find out
what
is in the data? In this demo we present P
erseus
, a large-scale system that enables the comprehensive analysis of large graphs by supporting the coupled summarization of graph properties and structures, guiding attention to outliers, and allowing the user to interactively explore normal and anomalous node behaviors.
Specifically, P
erseus
provides for the following operations: 1) It automatically extracts graph invariants (
e.g.
, degree, PageRank, real eigenvectors) by performing scalable, offline batch processing on H
adoop
; 2) It interactively visualizes univariate and bivariate distributions for those invariants; 3) It summarizes the properties of the nodes that the user selects; 4) It efficiently visualizes the induced subgraph of a selected node and its neighbors, by incrementally revealing its neighbors.
In our demonstration, we invite the audience to interact with P
erseus
to explore a variety of multi-million-edge social networks including a Wikipedia vote network, a friendship/foeship network in Slashdot, and a trust network based on the consumer review website Epinions.com.
The study of social network evolution has attracted many attentions from both the industry and academia. In this paper we demonstrate LaFT-Explorer, a general toolkit for explaining and reproducing the network growth process based on the friendship propagation. LaFT-Explorer presents multiple perspectives for analyzing the network evolution process and structure, including LaFT-Tree, LaFT-Trace and LaFT-Flow. Upon that we build LaFT-Rec, a new visualized interactive friend recommendation service based on the friendship propagation. LaFT-Rec not only shows whom one may make friends with, but also tells the user that why you should make friends with him and how you can reach him. We demonstrate our system built upon the academic social network of DBLP.
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