30,286 research outputs found
Economic Geography and the Evolution of Networks
An evolutionary perspective on economic geography requires a dynamic understanding of change in networks. This paper explores theories of network evolution for their use in geography and develops the conceptual framework of geographical network trajectories. It specifically assesses how tie selection constitutes the evolutionary process of retention and variation in network structure and how geography affects these mechanisms. Finally, a typology of regional network formations is used to discuss opportunities for innovation in and across regions.evolution, network trajectory, evolutionary economic geography, social network analysis, innovation
ESC: Edge-attributed Skyline Community Search in Large-scale Bipartite Graphs
Due to the ability of modeling relationships between two different types of
entities, bipartite graphs are naturally employed in many real-world
applications. Community Search in bipartite graphs is a fundamental problem and
has gained much attention. However, existing studies focus on measuring the
structural cohesiveness between two sets of vertices, while either completely
ignoring the edge attributes or only considering one-dimensional importance in
forming communities. In this paper, we introduce a novel community model, named
edge-attributed skyline community (ESC), which not only preserves the
structural cohesiveness but unravels the inherent dominance brought about by
multi-dimensional attributes on the edges of bipartite graphs. To search the
ESCs, we develop an elegant peeling algorithm by iteratively deleting edges
with the minimum attribute in each dimension. In addition, we also devise a
more efficient expanding algorithm to further reduce the search space and speed
up the filtering of unpromising vertices, where a upper bound is proposed and
proven. Extensive experiments on real-world large-scale datasets demonstrate
the efficiency, effectiveness, and scalability of the proposed ESC search
algorithms. A case study was conducted to compare with existing community
models, substantiating that our approach facilitates the precision and
diversity of results
Span-core Decomposition for Temporal Networks: Algorithms and Applications
When analyzing temporal networks, a fundamental task is the identification of
dense structures (i.e., groups of vertices that exhibit a large number of
links), together with their temporal span (i.e., the period of time for which
the high density holds). In this paper we tackle this task by introducing a
notion of temporal core decomposition where each core is associated with two
quantities, its coreness, which quantifies how densely it is connected, and its
span, which is a temporal interval: we call such cores \emph{span-cores}.
For a temporal network defined on a discrete temporal domain , the total
number of time intervals included in is quadratic in , so that the
total number of span-cores is potentially quadratic in as well. Our first
main contribution is an algorithm that, by exploiting containment properties
among span-cores, computes all the span-cores efficiently. Then, we focus on
the problem of finding only the \emph{maximal span-cores}, i.e., span-cores
that are not dominated by any other span-core by both their coreness property
and their span. We devise a very efficient algorithm that exploits theoretical
findings on the maximality condition to directly extract the maximal ones
without computing all span-cores.
Finally, as a third contribution, we introduce the problem of \emph{temporal
community search}, where a set of query vertices is given as input, and the
goal is to find a set of densely-connected subgraphs containing the query
vertices and covering the whole underlying temporal domain . We derive a
connection between this problem and the problem of finding (maximal)
span-cores. Based on this connection, we show how temporal community search can
be solved in polynomial-time via dynamic programming, and how the maximal
span-cores can be profitably exploited to significantly speed-up the basic
algorithm.Comment: ACM Transactions on Knowledge Discovery from Data (TKDD), 2020. arXiv
admin note: substantial text overlap with arXiv:1808.0937
A Method for Characterizing Communities in Dynamic Attributed Complex Networks
Many methods have been proposed to detect communities, not only in plain, but
also in attributed, directed or even dynamic complex networks. In its simplest
form, a community structure takes the form of a partition of the node set. From
the modeling point of view, to be of some utility, this partition must then be
characterized relatively to the properties of the studied system. However, if
most of the existing works focus on defining methods for the detection of
communities, only very few try to tackle this interpretation problem. Moreover,
the existing approaches are limited either in the type of data they handle, or
by the nature of the results they output. In this work, we propose a method to
efficiently support such a characterization task. We first define a
sequence-based representation of networks, combining temporal information,
topological measures, and nodal attributes. We then describe how to identify
the most emerging sequential patterns of this dataset, and use them to
characterize the communities. We also show how to detect unusual behavior in a
community, and highlight outliers. Finally, as an illustration, we apply our
method to a network of scientific collaborations.Comment: IEEE/ACM International Conference on Advances in Social Network
Analysis and Mining (ASONAM), P\'ekin : China (2014
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