22 research outputs found
API Requirements for Dynamic Graph Prediction
Given a large-scale time-evolving multi-modal and multi-relational complex network (a.k.a., a large-scale dynamic semantic graph), we want to implement algorithms that discover patterns of activities on the graph and learn predictive models of those discovered patterns. This document outlines the application programming interface (API) requirements for fast prototyping of feature extraction, learning, and prediction algorithms on large dynamic semantic graphs. Since our algorithms must operate on large-scale dynamic semantic graphs, we have chosen to use the graph API developed in the CASC Complex Networks Project. This API is supported on the back end by a semantic graph database (developed by Scott Kohn and his team). The advantages of using this API are (i) we have full-control of its development and (ii) the current API meets almost all of the requirements outlined in this document
Analyzing covert social network foundation behind terrorism disaster
This paper addresses a method to analyze the covert social network foundation
hidden behind the terrorism disaster. It is to solve a node discovery problem,
which means to discover a node, which functions relevantly in a social network,
but escaped from monitoring on the presence and mutual relationship of nodes.
The method aims at integrating the expert investigator's prior understanding,
insight on the terrorists' social network nature derived from the complex graph
theory, and computational data processing. The social network responsible for
the 9/11 attack in 2001 is used to execute simulation experiment to evaluate
the performance of the method.Comment: 17pages, 10 figures, submitted to Int. J. Services Science
Large scale network analysis with interactive visualisation
This paper proposes a new interactive visualisation for analysing large hierarchical structures and networks. The technique combines of different graph layout methods with a layout refinement process, an interactive navigation mechanism and clustering algorithms. The integration of these components makes it flexible in dealing with a variety of graph and hierarchical structures. Interactive exploration is enabled with chaincontext view. We aim to provide user with an effective mechanism for understanding of the nature of various networks. This could lead to the discovering and revealing of the hidden structures and relationships among elements as well as relationships associated with the elements. © 2009 IEEE
Visual analytics for supporting entity relationship discovery on text data
A*STAR Public Sector R
SSumM: Sparse Summarization of Massive Graphs
Given a graph G and the desired size k in bits, how can we summarize G within
k bits, while minimizing the information loss?
Large-scale graphs have become omnipresent, posing considerable computational
challenges. Analyzing such large graphs can be fast and easy if they are
compressed sufficiently to fit in main memory or even cache. Graph
summarization, which yields a coarse-grained summary graph with merged nodes,
stands out with several advantages among graph compression techniques. Thus, a
number of algorithms have been developed for obtaining a concise summary graph
with little information loss or equivalently small reconstruction error.
However, the existing methods focus solely on reducing the number of nodes, and
they often yield dense summary graphs, failing to achieve better compression
rates. Moreover, due to their limited scalability, they can be applied only to
moderate-size graphs.
In this work, we propose SSumM, a scalable and effective graph-summarization
algorithm that yields a sparse summary graph. SSumM not only merges nodes
together but also sparsifies the summary graph, and the two strategies are
carefully balanced based on the minimum description length principle. Compared
with state-of-the-art competitors, SSumM is (a) Concise: yields up to 11.2X
smaller summary graphs with similar reconstruction error, (b) Accurate:
achieves up to 4.2X smaller reconstruction error with similarly concise
outputs, and (c) Scalable: summarizes 26X larger graphs while exhibiting linear
scalability. We validate these advantages through extensive experiments on 10
real-world graphs.Comment: to be published in the 26th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD '20