On Community Outliers and their Efficient Detection in Information Networks
published: Oct. 1, 2010, recorded: July 2010, views: 4477
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Description
Linked or networked data are ubiquitous in many applications. Examples include web data or hypertext documents connected via hyperlinks, social networks or user profiles connected via friend links, co-authorship and citation information, blog data, movie reviews and so on. In these datasets (called "information networks"), closely related objects that share the same properties or interests form a community. For example, a community in blogsphere could be users mostly interested in cell phone reviews and news. Outlier detection in information networks can reveal important anomalous and interesting behaviors that are not obvious if community information is ignored. An example could be a low-income person being friends with many rich people even though his income is not anomalously low when considered over the entire population. This paper first introduces the concept of community outliers (interesting points or rising stars for a more positive sense), and then shows that well-known baseline approaches without considering links or community information cannot find these community outliers. We propose an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers. The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields (HMRF). Maximizing the data likelihood and the posterior of the model gives the solution to the outlier inference problem. We apply the model on both synthetic data and DBLP data sets, and the results demonstrate importance of this concept, as well as the effectiveness and efficiency of the proposed approach.
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Reviews and comments:
The topic of "Community Outliers and their Efficient Detection in Information Networks" focuses on identifying and understanding outliers or anomalous entities within communities or networks. Here's a breakdown of the key concepts in this topic:
Community Outliers:
In the context of information networks, a community refers to a group of interconnected nodes or entities that share common characteristics, interests, or relationships.
Community outliers <a href="https://poolcuebox.com/best-pool-cues-for-professional/">best pool cues for professionals </a> are entities that deviate significantly from the expected patterns within their respective communities.
Efficient Detection:
Efficient detection involves developing algorithms or methods that can quickly and accurately identify outliers within information networks.
Detecting outliers efficiently is crucial, especially in large-scale networks, to save computational resources and time.
Information Networks:
Information networks represent complex systems where nodes represent entities (such as users, websites, or nodes in a graph), and edges represent relationships or interactions between these entities.
Examples of information networks include social networks, citation networks, and communication networks.
Detection Techniques:
Various techniques can be employed to detect outliers in information networks. These may include statistical methods, machine learning algorithms, and network analysis approaches.
Anomalies can manifest in different ways, such as unusual behavior, unexpected connections, or atypical patterns of interaction.
Challenges:
Detecting outliers in information networks poses challenges, including the need to handle the dynamic nature of networks, scalability concerns for large networks, and the identification of subtle anomalies.
Applications:
Efficient detection of community outliers in information networks has practical applications in various fields, including cybersecurity (identifying suspicious network activity), social network analysis (detecting fake profiles or malicious behavior), and recommendation systems (identifying unusual patterns in user preferences).
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