Ensemble of Diverse Sparsifications for Link Prediction in Large-Scale Networks

Abstract Many real-world complex systems can be better modelled as multiplex networks, where the same individuals develop connections in multiple layers. Examples include social networks between individuals on multiple social networking platforms, and transportation networks between cities based on air, rail and road networks. Accurately predicting spurious links in multiplex networks is a challenging issue. In this article, we show that one can effectively use interlayer information to build an algorithm for spurious link prediction. We propose a similarity index that combines intralayer similarity with interlayer relevance for the link prediction purpose. The proposed similarity index is used to rank the node pairs, and identify those that are likely to be spurious. Our experimental results show that the proposed metric is much more accurate than intralayer similarity measures in correctly predicting the spurious links. The proposed method is an unsupervised method and has low computation complexity, and thus can be effectively applied for spurious link prediction in large-scale networks.

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Predicting missing links in directed networks based on local network structure and investment theory

International Journal of Modern Physics C ◽

10.1142/s0129183120500965 ◽

2020 ◽

Vol 31 (07) ◽

pp. 2050096

Author(s):

Jinsong Li ◽

Jianhua Peng ◽

Shuxin Liu ◽

Xinsheng Ji

Keyword(s):

Link Prediction ◽

Statistical Physics ◽

Large Scale ◽

Empirical Studies ◽

Local Network ◽

Directed Networks ◽

Local Index ◽

Investment Theory ◽

Large Scale Networks

As an elementary task in statistical physics and network science, link prediction has attracted great attention of researchers from many fields. While numerous similarity-based indices have been designed for undirected networks, link prediction in directed networks has not been thoroughly studied yet. Among several representative works, motif predictors such as “feed-forward-loop” and Bi-fan predictor perform well in both accuracy and efficiency. Nevertheless, they fail to explicitly explain the linkage motivation of nodes, nor do they consider the unequal contributions of different neighbors between node pairs. In this paper, motivated by the investment theory in economics, we propose a universal and explicable model to quantify the contributions of nodes on driving link formation. Based on the analysis on two typical investment relationships, namely “follow-up” and “co-follow”, an investment-profit index is designed for link prediction in directed networks. Empirical studies on 12 static networks and four temporal networks show that the proposed method outperforms eight mainstream baselines under three standard metrics. As a quasi-local index, it is also suitable for large-scale networks.

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SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5832 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4091-4098 ◽

Cited By ~ 1

Author(s):

Tao He ◽

Lianli Gao ◽

Jingkuan Song ◽

Xin Wang ◽

Kejie Huang ◽

...

Keyword(s):

Link Prediction ◽

Large Scale ◽

State Of The Art ◽

Network Embedding ◽

Compression Method ◽

Network Analytics ◽

Attributed Network ◽

Large Scale Networks ◽

Low Dimensional ◽

Embedding Methods

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is trained in an end-to-end fashion, which is able to dramatically compress the size of the trained embeddings, thus reduces storage footprint and accelerates retrieval speed. Our evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation. Moreover, the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.

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of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling

School of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling ◽

10.3389/978-2-88963-087-5 ◽

2019 ◽

Keyword(s):

Large Scale ◽

Brain Cells ◽

Neural Bases ◽

Large Scale Networks ◽

Camillo Golgi

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Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

Cerebral Cortex ◽

10.1093/cercor/bhab007 ◽

2021 ◽

Author(s):

Miguel Dasilva ◽

Christian Brandt ◽

Marc Alwin Gieselmann ◽

Claudia Distler ◽

Alexander Thiele

Keyword(s):

Attentional Control ◽

Large Scale ◽

Neuronal Excitability ◽

Cell Types ◽

Receptor Activation ◽

Frontal Eye Field ◽

Control Signals ◽

Cognitive Operations ◽

Eye Field ◽

Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 ◽

10.1145/2783258.2783385 ◽

2015 ◽

Cited By ~ 26

Author(s):

Michael Mitzenmacher ◽

Jakub Pachocki ◽

Richard Peng ◽

Charalampos Tsourakakis ◽

Shen Chen Xu

Keyword(s):

Large Scale ◽

Large Scale Networks

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied Network Science ◽

10.1007/s41109-021-00364-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Siddharth Arora ◽

Alexandra Brintrup

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Individual Performance ◽

Supply Network ◽

Supply Chain Networks ◽

Focal Firm ◽

Cooperative Competition ◽

Large Scale Networks ◽

The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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