11,899 research outputs found
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigate the
anomaly detection problem on graph time series. The temporal correlation is
modeled by the combination of recurrent neural network (RNN) and variational
inference (VI), while the spatial information is captured by the graph
convolutional network. In order to incorporate external factors, we use feature
extractor to augment the transition of latent variables, which can learn the
influence of external factors. With the target function as accumulative ELBO,
it is easy to extend this model to on-line method. The experimental study on
traffic flow data shows the detection capability of the proposed method
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
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