How do biological networks differ from social networks? (an experimental study)

Gutierrez-Bunster, Tatiana; Stege, Ulrike; Thomo, Alex; Taylor, John S.

doi:10.1109/asonam.2014.6921669

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Usually, we assign a vertex to each entity, and there is an edge between two entities if they are related or affect each other in some way. Analyzing graph structure has found applications in several important real-world problems such as targeted advertising [24], fraud detection [18], missing link prediction [16], locating functional modules of interacting proteins [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Can We Create Large k-Cores by Adding Few Edges?

Chitnis

Talmon

2018

Computer Science – Theory and Applications

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Introductionmentioning

confidence: 99%

Can We Create Large k-Cores by Adding Few Edges?

Chitnis

Talmon

2018

Computer Science – Theory and Applications

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“…It is well-known that networks across different domains exhibit a diverse set of properties. For example, biological networks such as protein interaction networks differ significantly from social networks in community structure and centrality measures [7]. Therefore, it is necessary to test dynamic link prediction methods in various domains outside of social networks.…”

Section: Introductionmentioning

confidence: 99%

Towards Better Evaluation for Dynamic Link Prediction

Poursafaei¹,

Huang²,

Pelrine³

et al. 2022

Preprint

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There has been recent success in learning from static graphs, but despite their prevalence, learning from time-evolving graphs remains challenging. We design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations and can better compare different methods' strengths and weaknesses. In particular, we create two visualization techniques to understand the recurring patterns of edges over time. They show that many edges reoccur at later time steps. Therefore, we propose a pure memorization baseline called EdgeBank. It achieves surprisingly strong performance across multiple settings, partly due to the easy negative edges used in the current evaluation setting. Hence, we introduce two more challenging negative sampling strategies that improve robustness and can better match real-world applications. Lastly, we introduce five new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research.Preprint. Under review.

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“…vertex) represents an individual or entity, and each edge represents the corresponding connection or relation. Capturing graph structure of data is useful in many applications, such as targeted advertising [41,43], knowledge distillation [45,46], data annotation [47][48][49], and protein analysis [17,39]. Each node in a graph often exhibits a crucial property -the importance or utility of a node depends on the number of connections between it and other nodes.…”

Section: Introductionmentioning

confidence: 99%

K-Core Decomposition on Super Large Graphs with Limited Resources

Gao¹,

Xu²,

Li³

et al. 2021

Preprint

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K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-andconquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.

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How do biological networks differ from social networks? (an experimental study)

Cited by 5 publications

References 29 publications

Can We Create Large k-Cores by Adding Few Edges?

Can We Create Large k-Cores by Adding Few Edges?

Towards Better Evaluation for Dynamic Link Prediction

K-Core Decomposition on Super Large Graphs with Limited Resources

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