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A hierarchical knowledge graph embedding framework for link prediction
Shuang Liu, Chengwang Hou, Jiana Meng, Peng Chen, Simon Kolmanič, 2024, izvirni znanstveni članek

Ključne besede: knowledge graph embedding, knowledge graph completion, negative sampling, link prediction
Objavljeno v DKUM: 04.02.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (1,76 MB)

Link prediction on Twitter
Sanda Martinčić-Ipšić, Edvin Močibob, Matjaž Perc, 2017, izvirni znanstveni članek

Opis: With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.
Ključne besede: link prediction, data mining, Twitter, network analysis
Objavljeno v DKUM: 15.09.2017; Ogledov: 1864; Prenosov: 212
Celotno besedilo (6,98 MB)
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Link prediction in multiplex online social networks
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, Matjaž Perc, 2017, izvirni znanstveni članek

Opis: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Ključne besede: social networks, complex networks, signed networks, link prediction, machine learning
Objavljeno v DKUM: 08.08.2017; Ogledov: 1598; Prenosov: 473
Celotno besedilo (940,17 KB)
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