scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Discovering spurious links in multiplex networks based on interlayer relevance

2019 ◽  
Vol 7 (5) ◽  
pp. 641-658 ◽  
Author(s):  
Zeynab Samei ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Link Prediction ◽  
Large Scale ◽  
Transportation Networks ◽  
Similarity Index ◽  
Similarity Measures ◽  
Computation Complexity ◽  
Multiplex Networks ◽  
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.

Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

2019 ◽  
Vol 18 (01) ◽  
pp. 241-286 ◽  
Author(s):  
Alper Ozcan ◽  
Sule Gunduz Oguducu
Keyword(s):  
Time Series ◽  
Real World ◽  
Link Prediction ◽  
Multivariate Time Series ◽  
Similarity Measures ◽  
Dynamic Structure ◽  
Social Bookmarking ◽  
Node Connectivity ◽  
Worldwide Web ◽  
Heterogeneous Social Networks

Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Real World ◽  
Large Scale ◽  
Convergence Rates ◽  
Parameter Tuning ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting

<p>Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application working at its best, its hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance. However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally expensive. Many of the automated hyper-parameter tuning techniques suffer from low convergence rates and high experimental time complexities. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms: random forest, extreme gradient boosting, and support vector machine. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values and is tested using a real-world educational dataset. Experimental results show that HyP-ABC is competitive with state-of-the-art techniques. Also, it has fewer hyper-parameters to be tuned than other population-based algorithms, making it worthwhile for real-world HPO problems.</p>

scholarly journals Link prediction in real-world multiplex networks via layer reconstruction method

2020 ◽  
Vol 7 (7) ◽  
pp. 191928
Author(s):  
Amir Mahdi Abdolhosseini-Qomi ◽  
Seyed Hossein Jafari ◽  
Amirheckmat Taghizadeh ◽  
Naser Yazdani ◽  
Masoud Asadpour ◽  
...  
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Body ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Reconstruction Method ◽  
Multiplex Networks ◽  
Technological Complex ◽  
Different Types

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

Link prediction based on nonequilibrium cooperation effect

2018 ◽  
Vol 32 (11) ◽  
pp. 1850128 ◽  
Author(s):  
LanXi Li ◽  
XuZhen Zhu ◽  
Hui Tian
Keyword(s):  
Numerical Analysis ◽  
Theoretical Analysis ◽  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Large Degree ◽  
Node Degree ◽  
Improve Accuracy

Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

EFFECTIVE GPU ACCELERATION OF LARGE SCALE, ASYNCHRONOUS SIMULATIONS ON GRAPHS

2012 ◽  
Vol 15 (08) ◽  
pp. 1250035
Author(s):  
DUSTIN ARENDT ◽  
YANG CAO
Keyword(s):  
Stochastic Modeling ◽  
Large Scale ◽  
Ad Hoc ◽  
General Purpose ◽  
Gpu Acceleration ◽  
Simulation Framework ◽  
Good Balance ◽  
Recent Emergence ◽  
Wide Range ◽  
Gpu Implementation

The recent emergence of GPGPU programming has resulted in a number of very efficient, but ultimately ad-hoc implementations of GPU accelerated simulations of complex systems. Because developing applications for the GPU is still a difficult and time consuming task, efficient GPU parallelizations of general purpose modeling frameworks are very useful. The dimer automaton is a stochastic modeling and simulation framework with a good balance of robustness, generality, and simplicity with capacity to model a wide range of phenomena. A major advantage of dimer automata is the ease in which they can be applied to any space that can be represented as a graph. Therefore, we have developed an efficient GPU implementation of dimer automata that runs up to 80 times faster than the serial implementation.

scholarly journals Use cases for COVID-19 screening and surveillance with rapid antigen-detecting tests: a systematic review

2021 ◽  
Author(s):  
Apoorva Anand ◽  
Jacob Bigio ◽  
Emily MacLean ◽  
Talya Underwood ◽  
Nitika Pant Pai ◽  
...  
Keyword(s):  
Systematic Review ◽  
Real World ◽  
Large Scale ◽  
Low Cost ◽  
Risk Of Bias ◽  
Use Cases ◽  
Global Alliance ◽  
Prevalence Survey ◽  
Screening And Surveillance ◽  
Wide Range

Introduction: Testing is critical to controlling the COVID-19 pandemic. Antigen-detecting rapid diagnostic tests (Ag-RDTs) that can be used at the point of care have the potential to increase access to COVID 19 testing, particularly in settings with limited laboratory capacity. This systematic review synthesized literature on specific use cases and performance of Ag RDTs for detecting SARS-CoV-2, for the first comprehensive assessment of Ag RDT use in real-world settings. Methods: We searched three databases (PubMed, EMBASE and medRxiv) up to 12 April 2021 for publications on Ag-RDT use for large-scale screening, irrespective of symptoms, and surveillance of COVID-19, excluding studies of only presumptive COVID-19 patients. We tabulated data on the study setting, populations, type of test, diagnostic performance and operational findings. We assessed risk of bias using QUADAS-2 and an adapted tool for prevalence studies. Results: From 4313 citations, 39 studies conducted in asymptomatic and symptomatic adults were included. Study sample sizes varied from 40 to >5 million. Of 39 studies, 37 (94.9%) investigated lateral flow Ag-RDTs and two (5.1%) investigated multiplex sandwich chemiluminescent enzyme immunoassay Ag-RDTs. Six categories of testing (screening/surveillance) initiatives were identified: mass screening (n=13), targeted screening (n=11), healthcare entry testing (n=6), at-home testing (n=4), surveillance (n=4) and prevalence survey (n=1). Across studies, Ag-RDT sensitivity varied from 40% to 100%. Ag-RDTs were noted as convenient, easy-to-use and low cost, with a rapid turnaround time and high user acceptability. Risk of bias was generally low or unclear across the studies. Conclusion: This systematic review demonstrates the use of Ag-RDTs across a wide range of real-world settings for screening and surveillance of COVID-19 in both symptomatic and asymptomatic individuals. Ag-RDTs were overall found to be easy-to-use, low cost and rapid tools, when consideration is given to their implementation and interpretation. The review was funded by FIND, the global alliance for diagnostics.

An adaptive node embedding framework for multiplex networks

2021 ◽  
Vol 25 (2) ◽  
pp. 483-503
Author(s):  
Nianwen Ning ◽  
Yilin Yang ◽  
Chenguang Song ◽  
Bin Wu
Keyword(s):  
Random Walk ◽  
Real World ◽  
Link Prediction ◽  
Sampling Bias ◽  
Cross Layer ◽  
Sampling Strategies ◽  
Multiplex Networks ◽  
Cross Domain ◽  
Edge Distribution ◽  
High Degree

Network Embedding (NE) has emerged as a powerful tool in many applications. Many real-world networks have multiple types of relations between the same entities, which are appropriate to be modeled as multiplex networks. However, at random walk-based embedding study for multiplex networks, very little attention has been paid to the problems of sampling bias and imbalanced relation types. In this paper, we propose an Adaptive Node Embedding Framework (ANEF) based on cross-layer sampling strategies of nodes for multiplex networks. ANEF is the first framework to focus on the bias issue of sampling strategies. Through metropolis hastings random walk (MHRW) and forest fire sampling (FFS), ANEF is less likely to be trapped in local structure with high degree nodes. We utilize a fixed-length queue to record previously visited layers, which can balance the edge distribution over different layers in sampled node sequence processes. In addition, to adaptively sample the cross-layer context of nodes, we also propose a node metric called Neighbors Partition Coefficient (NPC). Experiments on real-world networks in diverse fields show that our framework outperforms the state-of-the-art methods in application tasks such as cross-domain link prediction and mutual community detection.

scholarly journals The Effect of Mandatory Play Breaks on Subsequent Gambling Behavior Among Norwegian Online Sports Betting, Slots and Bingo Players: A Large-scale Real World Study

2021 ◽  
Author(s):  
Niklas Hopfgartner ◽  
Michael Auer ◽  
Tiago Santos ◽  
Denis Helic ◽  
Mark D. Griffiths
Keyword(s):  
Real World ◽  
Large Scale ◽  
Responsible Gambling ◽  
Gambling Behavior ◽  
Sports Betting ◽  
Forced Termination ◽  
Personalized Feedback ◽  
Wide Range ◽  
Gambling Expenditure ◽  
Gambling Operators

AbstractIn order to protect gamblers, gambling operators have introduced a wide range of responsible gambling (RG) tools. Mandatory play breaks (i.e., forced termination of a gambling session) and personalized feedback about the gambling expenditure are two RG tools that are frequently used. While the motivation behind mandatory play breaks is simple (i.e., gambling operators expect gamblers to reduce their gambling significantly as a result of an enforced break in play), empirical evidence supporting the efficacy of the mandatory breaks is still limited. The present study comprised a real-world experiment with the clientele of Norwegian gambling operator Norsk Tipping. On the Norsk Tipping gambling website, which offers slots, bingo and sports-betting, forced termination occurs if gamblers have played continuously for a one-hour period. The study tested the effect of different lengths of mandatory play breaks (90 s, 5 min, 15 min) on subsequent gambling behavior, as well as the effect of combined personalized feedback concerning money wagered, won, and net win/loss. In total 21,129 online players (61% male; mean age = 47.4 years) experienced at least one play break between April 17 and May 21 (2020) with 156,989 mandatory play breaks in total. Results indicated that a 15-min mandatory play break led to a disproportionately longer voluntary play pause compared to 5-min and 90-s mandatory play breaks. Personalized feedback appeared to have no additional effect on subsequent gambling and none of the mandatory play breaks appeared to affect the increase or decrease in money wagered once players started to gamble again.

scholarly journals Link Prediction through Deep Generative Model

2018 ◽  
Author(s):  
Xu-Wen Wang ◽  
Yize Chen ◽  
Yang-Yu Liu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Generative Models ◽  
Generative Model ◽  
Superior Performance ◽  
Prediction Problem ◽  
Structural Patterns ◽  
Domain Specific

AbstractInferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1–3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7–9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13–17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance.SummaryWe propose a new link prediction method based on deep generative models.

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