Collective classification in social networks
Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that coul...
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| Abstract | Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that could only be captured by analyzing the features of an item and its interactions. Collective classification methods use both structure and attributes, often by aggregating data from neighbors of a node and learning a model on the aggregated data. In social networks, the degree distribution of nodes follows a power law where few nodes have many neighbors. High degree nodes have incoming links from low degree nodes of different classes and many nodes have very few edges. Hence, using only local structure may lead to poor predictions. Also, many social networks allow for different types of interactions (retweet, reply, like, etc.) that affect classification differently. This article proposes a collective classification method that makes use of the structure of a network to determine its neighbors. It then presents experiments aimed at detecting jihadi propagandists and malware distributors on social networks. |
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| AbstractList | Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that could only be captured by analyzing the features of an item and its interactions. Collective classification methods use both structure and attributes, often by aggregating data from neighbors of a node and learning a model on the aggregated data. In social networks, the degree distribution of nodes follows a power law where few nodes have many neighbors. High degree nodes have incoming links from low degree nodes of different classes and many nodes have very few edges. Hence, using only local structure may lead to poor predictions. Also, many social networks allow for different types of interactions (retweet, reply, like, etc.) that affect classification differently. This article proposes a collective classification method that makes use of the structure of a network to determine its neighbors. It then presents experiments aimed at detecting jihadi propagandists and malware distributors on social networks. |
| Author | Birregah, Babiga Jaafor, Omar |
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| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
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| Keywords | Semi-supervised classification unbalanced data collective classification surveillance multi-layer networks social networks |
| Language | English |
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| SubjectTerms | collective classification Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social media Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social networks Human-centered computing -- Human computer interaction (HCI) Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms -- Web-based interaction Information systems Information systems -- Information systems applications Information systems -- Information systems applications -- Collaborative and social computing systems and tools Information systems -- Information systems applications -- Collaborative and social computing systems and tools -- Social networking sites Information systems -- Information systems applications -- Data mining Information systems -- World Wide Web Information systems -- World Wide Web -- Web applications Information systems -- World Wide Web -- Web applications -- Social networks multi-layer networks Networks Networks -- Network types Networks -- Network types -- Overlay and other logical network structures Networks -- Network types -- Overlay and other logical network structures -- Online social networks Semi-supervised classification social networks surveillance unbalanced data |
| Title | Collective classification in social networks |
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