Analyzing short texts infers discriminative and coherent latent topics that is a critical and
fundamental task since many real-world applications require semantic understanding of …

Graph data have become increasingly important, and graph node clustering has emerged
as a fundamental task in data analysis. In recent years, graph node clustering has gradually …

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly
embraced by individuals, groups, and organizations as a valuable source of information …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …

Detecting local events (eg, protest, disaster) at their onsets is an important task for a wide
spectrum of applications, ranging from disaster control to crime monitoring and place …

Background Internet provides different tools for communicating with patients, such as social
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …

With the emergence of microblogging platforms and social media applications, large
amounts of user-generated data in the form of comments, reviews, and brief text messages …

Short text clustering is an increasingly important methodology but faces the challenges of
sparsity and high-dimensionality of text data. Previous concept decomposition methods …

Clustering short text streams is a challenging task due to its unique properties: infinite
length, sparse data representation and cluster evolution. Existing approaches often exploit …

With increasing popularity of social media, Twitter has become one of the leading platforms
to report events in real-time. Detecting events from Twitter stream requires complex …