SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

Nechaev, Yaroslav; Corcoglioniti, Francesco; Giuliano, Claudio

doi:10.1007/s13748-018-0160-x

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…It is an automated system that collects its input from various SPARQL endpoints and web APIs (Kettouch et al, 2019). SocialLink is a framework that automatically connects DBpedia entities to equivalent Twitter profiles (Nechaev et al, 2018). Framework XOSM was developed to integrate and query Open Street Map and Open Linked Geo Data resources.…”

Section: Lod Enabled Rsmentioning

confidence: 99%

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

Subramaniyaswamy

Natarajan

Gandomi

2020

Expert Systems with Applications

226

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Section: Lod Enabled Rsmentioning

confidence: 99%

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

Subramaniyaswamy

Natarajan

Gandomi

2020

Expert Systems with Applications

226

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“…Social graphs represents users as nodes and relations such as friendship or co-authorship as arcs. User representations are of great interest in a variety of tasks, for example to detect whether an actor in the graph is the potential source of misinformation or unhealthy behavior [77,80]. For these reasons, social networks are arguably the richest source of information for graph learning methods, in that a vast amount of features are available for each user.…”

Section: Social Networkmentioning

confidence: 99%

A Gentle Introduction to Deep Learning for Graphs

Bacciu,

Errica,

Micheli

et al. 2019

Preprint

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The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs.The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.

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“…Social graph, which is a graph of connections between the users, is shown to be an important feature for many tasks on social media, for example, user profiling. We followed the recently proposed approach (Nechaev et al, 2018a) to acquire 300dimensional dense user representations based on a social graph. This feature, however, did not improve the performance of our approach and was excluded.…”

Section: User-based Featuresmentioning

confidence: 99%