21,836 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
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
Graph based Anomaly Detection and Description: A Survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field
Comparative Evaluation of Community Detection Algorithms: A Topological Approach
Community detection is one of the most active fields in complex networks
analysis, due to its potential value in practical applications. Many works
inspired by different paradigms are devoted to the development of algorithmic
solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance
measure considering the community structure as a partition (Rand Index,
Normalized Mutual information, etc.). However, this type of comparison neglects
the topological properties of the communities. In this article, we present a
comprehensive comparative study of a representative set of community detection
methods, in which we adopt both types of evaluation. Community-oriented
topological measures are used to qualify the communities and evaluate their
deviation from the reference structure. In order to mimic real-world systems,
we use artificially generated realistic networks. It turns out there is no
equivalence between both approaches: a high performance does not necessarily
correspond to correct topological properties, and vice-versa. They can
therefore be considered as complementary, and we recommend applying both of
them in order to perform a complete and accurate assessment
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
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