In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by random subspace techniques and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Experimental r...

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve...

Numerous applications in dynamic social networks, ranging from telecommunications to financial transactions, create evolving datasets. Detecting outliers in such dynamic networks is inherently challenging, because the arbitrary linkage structure with massive information is changing over time. Little research has been done on detecting outliers for dynamic social networks, even then, they repres...

How can we accurately estimate local triangles for all nodes in simple and multigraph streams? Local triangle counting in a graph stream is one of the most fundamental tasks in graph mining with important applications including anomaly detection and social network analysis. Although there have been several local triangle counting methods in a graph stream, their estimation has a large variance ...

There is a demand for computational methods that can extract meaningful patterns from social networks in real time. However, these networks can be extremely large and volatile, and brute force algorithms for high-dimensional data analysis are intractable as high costs and poor runtime preclude many real-world applications. I present two online (sequentially updating) strategies that from learn ...

Social network analysis has attracted intensive interests by researchers from multiple disciplines. However most of the existing work is descriptive nature, and statistical network analysis remains an active area of research. In this paper, we model and study two facets of the social networks in short message services (SMS). One is the structure of the contact networks of mobile users, the othe...

E-mail is one of the most popular and widely used form of electronic communication used today. The patterns in the social interactions or contacts between people by email can be analysed using social network analysis and user behaviour analysis. In this paper we provide a review of the work related to the areas of dynamic modelling and link prediction of social networks, and anomaly detection f...

Developing a framework for analysis of large scale masscommunication media such as instant messaging (popularly known as IM) has gone largely unexplored up until this point. This paper explores various data mining issues and how they relate to Instant Messaging and current CounterTerrorism efforts. Specific topics include user pattern analysis, anomaly detection, limited message size based text...

Educational achievement is positively correlated with religious devotion. However, those religious sects which are more devoted have, on average, lower levels of education. Evangelical Protestants, for example, have the highest level of religious devotion but the lowest level of educational achievement. Previous research explains this by proposing that education enhances the gains to social int...

Uncovering Interesting Attributed Anomalies in Large Graphs Nan Li Graph is a fundamental model for capturing entities and their relations in a wide range of applications. Examples of real-world graphs include the Web, social networks, communication networks, intrusion networks, collaboration networks, and biological networks. In recent years, with the proliferation of rich information availabl...