3 research outputs found
Higher Order Temporal Analysis of Global Terrorism Data
Temporal networks are a fundamental and flexible way of describing the
activities, relationships, and evolution of any complex system. Global
terrorism is one of the biggest concerns of recent times. It is also an example
of a temporal network that evolves over time. Graph analytics can be used to
explore salient properties of the terrorism network to understand its modus
operandi, which can be used by the global alliance of security and government
entities to form a co-ordinated response to this threat. We present graph based
analysis to understand temporal evolution of global terrorism using the Global
Terrorism Database (GTD).Comment: 2019 IEEE Big Data Conference, 3rd workshop on Graph Techniques for
Adversarial Activity Analytic
Semantic Property Graph for Scalable Knowledge Graph Analytics
Graphs are a natural and fundamental representation of describing the
activities, relationships, and evolution of various complex systems. Many
domains such as communication, citation, procurement, biology, social media,
and transportation can be modeled as a set of entities and their relationships.
Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two
of the most used data models to encode information in a graph. Both models are
similar in terms of using basic graph elements such as nodes and edges but
differ in terms of modeling approach, expressibility, serialization, and target
applications. RDF is a flexible data exchange model for expressing information
about entities but it tends to a have high memory footprint and inefficient
storage, which does not make it a natural choice to perform scalable graph
analytics. In contrast, LPG has gained traction as a reliable model in
performing scalable graph analytic tasks such as sub-graph matching, network
alignment, and real-time knowledge graph query. It provides efficient storage,
fast traversal, and flexibility to model various real-world domains. At the
same time, the LPGs lack the support of a formal knowledge representation such
as an ontology to provide automated knowledge inference. We propose Semantic
Property Graph (SPG) as a logical projection of reified RDF into LPG model. SPG
continues to use RDF ontology to define type hierarchy of the projected graph
and validate it against a given ontology. We present a framework to convert
reified RDF graphs into SPG using two different computing environments. We also
present cloud-based graph migration capabilities using Amazon Web Services
ITeM: Independent Temporal Motifs to Summarize and Compare Temporal Networks
Networks are a fundamental and flexible way of representing various complex
systems. Many domains such as communication, citation, procurement, biology,
social media, and transportation can be modeled as a set of entities and their
relationships. Temporal networks are a specialization of general networks where
the temporal evolution of the system is as important to understand as the
structure of the entities and relationships. We present the Independent
Temporal Motif (ITeM) to characterize temporal graphs from different domains.
The ITeMs are edge-disjoint temporal motifs that can be used to model the
structure and the evolution of the graph. For a given temporal graph, we
produce a feature vector of ITeM frequencies and apply this distribution to the
task of measuring the similarity of temporal graphs. We show that ITeM has
higher accuracy than other motif frequency-based approaches. We define various
metrics based on ITeM that reveal salient properties of a temporal network. We
also present importance sampling as a method for efficiently estimating the
ITeM counts. We evaluate our approach on both synthetic and real temporal
networks