57,383 research outputs found
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Discriminative Link Prediction using Local Links, Node Features and Community Structure
A link prediction (LP) algorithm is given a graph, and has to rank, for each
node, other nodes that are candidates for new linkage. LP is strongly motivated
by social search and recommendation applications. LP techniques often focus on
global properties (graph conductance, hitting or commute times, Katz score) or
local properties (Adamic-Adar and many variations, or node feature vectors),
but rarely combine these signals. Furthermore, neither of these extremes
exploit link densities at the intermediate level of communities. In this paper
we describe a discriminative LP algorithm that exploits two new signals. First,
a co-clustering algorithm provides community level link density estimates,
which are used to qualify observed links with a surprise value. Second, links
in the immediate neighborhood of the link to be predicted are not interpreted
at face value, but through a local model of node feature similarities. These
signals are combined into a discriminative link predictor. We evaluate the new
predictor using five diverse data sets that are standard in the literature. We
report on significant accuracy boosts compared to standard LP methods
(including Adamic-Adar and random walk). Apart from the new predictor, another
contribution is a rigorous protocol for benchmarking and reporting LP
algorithms, which reveals the regions of strengths and weaknesses of all the
predictors studied here, and establishes the new proposal as the most robust.Comment: 10 pages, 5 figure
Applications of Structural Balance in Signed Social Networks
We present measures, models and link prediction algorithms based on the
structural balance in signed social networks. Certain social networks contain,
in addition to the usual 'friend' links, 'enemy' links. These networks are
called signed social networks. A classical and major concept for signed social
networks is that of structural balance, i.e., the tendency of triangles to be
'balanced' towards including an even number of negative edges, such as
friend-friend-friend and friend-enemy-enemy triangles. In this article, we
introduce several new signed network analysis methods that exploit structural
balance for measuring partial balance, for finding communities of people based
on balance, for drawing signed social networks, and for solving the problem of
link prediction. Notably, the introduced methods are based on the signed graph
Laplacian and on the concept of signed resistance distances. We evaluate our
methods on a collection of four signed social network datasets.Comment: 37 page
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.Comment: CIKM 201
Multi-Scale Link Prediction
The automated analysis of social networks has become an important problem due
to the proliferation of social networks, such as LiveJournal, Flickr and
Facebook. The scale of these social networks is massive and continues to grow
rapidly. An important problem in social network analysis is proximity
estimation that infers the closeness of different users. Link prediction, in
turn, is an important application of proximity estimation. However, many
methods for computing proximity measures have high computational complexity and
are thus prohibitive for large-scale link prediction problems. One way to
address this problem is to estimate proximity measures via low-rank
approximation. However, a single low-rank approximation may not be sufficient
to represent the behavior of the entire network. In this paper, we propose
Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can
handle massive networks. The basis idea of MSLP is to construct low rank
approximations of the network at multiple scales in an efficient manner. Based
on this approach, MSLP combines predictions at multiple scales to make robust
and accurate predictions. Experimental results on real-life datasets with more
than a million nodes show the superior performance and scalability of our
method.Comment: 20 pages, 10 figure
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