348,618 research outputs found
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar
purposes, etc., community detection is an important and extremely useful tool
in both scientific inquiry and data analytics. However, the classic methods of
community detection, such as spectral clustering and statistical inference, are
falling by the wayside as deep learning techniques demonstrate an increasing
capacity to handle high-dimensional graph data with impressive performance.
Thus, a survey of current progress in community detection through deep learning
is timely. Structured into three broad research streams in this domain - deep
neural networks, deep graph embedding, and graph neural networks, this article
summarizes the contributions of the various frameworks, models, and algorithms
in each stream along with the current challenges that remain unsolved and the
future research opportunities yet to be explored.Comment: Accepted Paper in the 29th International Joint Conference on
Artificial Intelligence (IJCAI 20), Survey Trac
Adversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i.e., graphs with
imperceptible perturbations added, can cause deep graph models to fail on
node/graph classification tasks. In this paper, we extend adversarial graphs to
the problem of community detection which is much more difficult. We focus on
black-box attack and aim to hide targeted individuals from the detection of
deep graph community detection models, which has many applications in
real-world scenarios, for example, protecting personal privacy in social
networks and understanding camouflage patterns in transaction networks. We
propose an iterative learning framework that takes turns to update two modules:
one working as the constrained graph generator and the other as the surrogate
community detection model. We also find that the adversarial graphs generated
by our method can be transferred to other learning based community detection
models.Comment: In Proceedings of The Web Conference 2020, April 20-24, 2020, Taipei,
Taiwan. 11 page
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks
Detection of Deception in a Virtual World
This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.</jats:p
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