Titlebook: Complex Networks and Their Applications VII; Volume 2 Proceedings Luca Maria Aiello,Chantal Cherifi,Luis M. Rocha Conference proceedings 20

连系

Learning Structural Node Representations on Directed Graphsle information about nodes has recently gained a lot of attention. A state-of-the-art algorithm, ., generates such representations for the nodes of undirected networks. However, the algorithm is unable to handle directed, weighted networks. In this paper, we present ., a generalization of the above

VEIL

Automatic Identification of Component Roles in Software Design Networks piece of software) and each link represents a software code-related dependency between two classes. This work provides two main contributions. First, we reveal how typical software networks exhibit a structure very similar to other real-world networks: they are sparse, scale-free and have low avera

直觉没有

initiate

Explicit Feedbacks Meet with Implicit Feedbacks: A Combined Approach for Recommendation Systemng similarity, the presence of users’ implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users’ embedding, that helps better rating prediction of users. Most existing recommender systems focus on modeling of ratings

存心

1860-949X ions, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.978-3-030-05414-4Series ISSN 1860-949X Series E-ISSN 1860-9503

尽责

Characterizing Temporal Bipartite Networks - Sequential- Versus Cross-Taskinge investigate the relation (rank correlation) between the two sequential-tasking measures and another 10 nodal features. Users that interact less frequently or more regularly in time (low deviation in the time interval between two interactions) or with fewer items tend to be more sequential-tasking

毗邻

Endometrium

A Compressive Sensing Framework for Distributed Detection of High Closeness Centrality Nodes in Netw top-. closeness centrality nodes in the network using the proposed local metric. Computations of our ego-centric metric and the aggregation procedure are both carried out effectively in a distributed manner, using only local interactions between neighboring nodes. The performance of the proposed me

cumulative

Bringing a Feature Selection Metric from Machine Learning to Complex Networksze of the communities whereas the ones we propose are by definition linked to the density of the community. This hence makes their results comparable from one network to another. Finally, the parameter-free selection process applied to nodes allows for a universal system, contrary to the thresholds

Nucleate

Automatic Identification of Component Roles in Software Design Networkse software system. We use a role taxonomy from literature which defines six so-called archetypes of software classes, which, once assigned to a class, can provide useful insights for engineers. In this paper we train and validate a model that is able to automatically assess which of these archetypes