We analyse the darkwebOKeeffe, Kevin P. and find its structure is unusual.Griffith, Virgil For example, $$ \sim 87 $$\%$$ of darkweb sites never link to another site. To call a “web” thus misnomer—it’s better described as set largely isolated dark silos.Xu, Yang As we show through detailed comparison World Wide Web (www),Santi, Paolo this siloed highly dissimilar other social networks indicates...

Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe ...

Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applicat...

technology has had many influences on the iranian society in different eras. today, information and telecommunication technologies have revolutionized the basic structures of society and a new network society has been propagated inside the traditional and semi-modern context. in countries like iran, which are in the process of modernization, a chaos deriving from the transition period is observ...

In this work, we propose an unsupervised approach for crowd scene anomaly detection and localization using a social network model. Using a window-based approach, a video scene is first partitioned at spatial and temporal levels, and a set of spatio-temporal cuboids is constructed. Objects exhibiting scene dynamics are detected and the crowd behavior in each cuboid is modeled using local social ...

In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate mu...

Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, a...