61,342 research outputs found
Attributed Network Embedding for Learning in a Dynamic Environment
Network embedding leverages the node proximity manifested to learn a
low-dimensional node vector representation for each node in the network. The
learned embeddings could advance various learning tasks such as node
classification, network clustering, and link prediction. Most, if not all, of
the existing works, are overwhelmingly performed in the context of plain and
static networks. Nonetheless, in reality, network structure often evolves over
time with addition/deletion of links and nodes. Also, a vast majority of
real-world networks are associated with a rich set of node attributes, and
their attribute values are also naturally changing, with the emerging of new
content patterns and the fading of old content patterns. These changing
characteristics motivate us to seek an effective embedding representation to
capture network and attribute evolving patterns, which is of fundamental
importance for learning in a dynamic environment. To our best knowledge, we are
the first to tackle this problem with the following two challenges: (1) the
inherently correlated network and node attributes could be noisy and
incomplete, it necessitates a robust consensus representation to capture their
individual properties and correlations; (2) the embedding learning needs to be
performed in an online fashion to adapt to the changes accordingly. In this
paper, we tackle this problem by proposing a novel dynamic attributed network
embedding framework - DANE. In particular, DANE first provides an offline
method for a consensus embedding and then leverages matrix perturbation theory
to maintain the freshness of the end embedding results in an online manner. We
perform extensive experiments on both synthetic and real attributed networks to
corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page
Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks
Modularity for multilayer networks, also called multislice modularity, is
parametric to a resolution factor and an inter-layer coupling factor. The
former is useful to express layer-specific relevance and the latter quantifies
the strength of node linkage across the layers of a network. However, such
parameters can be set arbitrarily, thus discarding any structure information at
graph or community level. Other issues are related to the inability of properly
modeling order relations over the layers, which is required for dynamic
networks.
In this paper we propose a new definition of modularity for multilayer
networks that aims to overcome major issues of existing multislice modularity.
We revise the role and semantics of the layer-specific resolution and
inter-layer coupling terms, and define parameter-free unsupervised approaches
for their computation, by using information from the within-layer and
inter-layer structures of the communities. Moreover, our formulation of
multilayer modularity is general enough to account for an available ordering of
the layers and relating constraints on layer coupling. Experimental evaluation
was conducted using three state-of-the-art methods for multilayer community
detection and nine real-world multilayer networks. Results have shown the
significance of our modularity, disclosing the effects of different
combinations of the resolution and inter-layer coupling functions. This work
can pave the way for the development of new optimization methods for
discovering community structures in multilayer networks.Comment: Accepted at the IEEE/ACM Conf. on Advances in Social Network Analysis
and Mining (ASONAM 2017
A General Framework for Complex Network Applications
Complex network theory has been applied to solving practical problems from
different domains. In this paper, we present a general framework for complex
network applications. The keys of a successful application are a thorough
understanding of the real system and a correct mapping of complex network
theory to practical problems in the system. Despite of certain limitations
discussed in this paper, complex network theory provides a foundation on which
to develop powerful tools in analyzing and optimizing large interconnected
systems.Comment: 8 page
A Network Topology Approach to Bot Classification
Automated social agents, or bots, are increasingly becoming a problem on
social media platforms. There is a growing body of literature and multiple
tools to aid in the detection of such agents on online social networking
platforms. We propose that the social network topology of a user would be
sufficient to determine whether the user is a automated agent or a human. To
test this, we use a publicly available dataset containing users on Twitter
labelled as either automated social agent or human. Using an unsupervised
machine learning approach, we obtain a detection accuracy rate of 70%
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