6,589 research outputs found
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Online Social Networks: Measurements, Analysis and Solutions for Mining Challenges
In the last decade, online social networks showed enormous growth. With the rise
of these networks and the consequent availability of wealth social network data, Social
Network Analysis (SNA) led researchers to get the opportunity to access, analyse and
mine the social behaviour of millions of people, explore the way they communicate and
exchange information.
Despite the growing interest in analysing social networks, there are some challenges
and implications accompanying the analysis and mining of these networks. For example,
dealing with large-scale and evolving networks is not yet an easy task and still requires
a new mining solution. In addition, finding communities within these networks is a
challenging task and could open opportunities to see how people behave in groups on a
large scale. Also, the challenge of validating and optimizing communities without knowing
in advance the structure of the network due to the lack of ground truth is yet another
challenging barrier for validating the meaningfulness of the resulting communities.
In this thesis, we started by providing an overview of the necessary background and key
concepts required in the area of social networks analysis. Our main focus is to provide
solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive
technique to help in the prediction of the execution time of the analysis tasks for
evolving networks through employing predictive modeling techniques to the problem of
evolving and large-scale networks. Second, we study the performance of existing community
detection approaches to derive high quality community structure using a real email
network through analysing the exchange of emails and exploring community dynamics.
The aim is to study the community behavioral patterns and evaluate their quality within
an actual network. Finally, we propose an ensemble technique for deriving communities
using a rich internal enterprise real network in IBM that reflects real collaborations
and communications between employees. The technique aims to improve the community
detection process through the fusion of different algorithms
Applications of Cohesive Subgraph Detection Algorithms to Analyzing Socio-Technical Networks
Socio-technical networks can be productively modeled at several granularities, including the interaction of actors, how this interaction is mediated by digital artifacts, and sociograms that model direct ties between the actors themselves. Cohesive subgraph detection algorithms (CSDA, a.k.a. “community detection algorithms”) are often applied to sociograms, but also have utility in analyzing graphs corresponding to other levels of modeling. This paper illustrates applications of CSDA to graphs modeling interaction and mediated association. It reviews some leading candidate algorithms (particularly InfoMap, link communities, the Louvain method, and weakly connected components, all of which are available in R), and evaluates them with respect to how useful they have been in analyzing a large dataset derived from a network of educators known as Tapped In. This practitioner-oriented evaluation is a complement to more formal benchmark based studies common in the literature
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