scholarly journals Adaptive Network Automata Modelling of Complex Networks

2021 ◽  
Author(s):  
Alessandro Muscoloni ◽  
Umberto Michieli ◽  
Carlo Vittorio Cannistraci
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Network Science ◽  
Fundamental Problem ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Self Organization ◽  
Deterministic Models ◽  
Adaptive Network ◽  
Wide Range

Many complex networks have a connectivity that might be only partially detected or that tends to grow over time, hence the prediction of non-observed links is a fundamental problem in network science. The aim of topological link prediction is to forecast these non-observed links by only exploiting features intrinsic to the network topology. It has a wide range of real applications, like suggesting friendships in social networks or predicting interactions in biological networks.The Cannistraci-Hebb theory is a recent achievement in network science that includes a theoretical framework to understand local-based link prediction on paths of length n. In this study we introduce two innovations: theory of modelling (science) and theory of realization (engineering). For the theory of modelling we first recall a definition of network automata as a general framework for modelling the growth of connectivity in complex networks. We then show that several deterministic models previously developed fall within this framework and we introduce novel network automata following the Cannistraci-Hebb rule. For the theory of realization, we present how to build adaptive network automata for link prediction, which incorporate multiple deterministic models of self-organization and automatically choose the rule that better explains the patterns of connectivity in the network under investigation. We compare Cannistraci-Hebb adaptive (CHA) network automaton against state-of-the-art link prediction methods such as structural perturbation method (SPM), stochastic block models (SBM) and artificial intelligence algorithms for graph embedding. CHA displays an overall higher link prediction performance across different evaluation frameworks on 1386 networks. Finally, we highlight that CHA offers the key advantage to explicitly explain the mechanistic rule of self-organization which leads to the link prediction performance, whereas SPM and graph embedding not. In comparison to CHA, SBM unfortunately shows irrelevant and unsatisfactory performance demonstrating that SBM modelling is not adequate for link prediction in real networks.

scholarly journals Adaptive Network Automata Modelling of Complex Networks

2020 ◽  
Author(s):  
Alessandro Muscoloni ◽  
Umberto Michieli ◽  
Carlo Vittorio Cannistraci
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Network Science ◽  
Fundamental Problem ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Self Organization ◽  
Deterministic Models ◽  
Adaptive Network ◽  
Wide Range

Many complex networks have a connectivity that might be only partially detected or that tends to grow over time, hence the prediction of non-observed links is a fundamental problem in network science. The aim of topological link prediction is to forecast these non-observed links by only exploiting features intrinsic to the network topology. It has a wide range of real applications, like suggesting friendships in social networks or predicting interactions in biological networks.The Cannistraci-Hebb theory is a recent achievement in network science that includes a theoretical framework to understand local-based link prediction on paths of length n. In this study we introduce two innovations: theory of modelling (science) and theory of realization (engineering). For the theory of modelling we first recall a definition of network automata as a general framework for modelling the growth of connectivity in complex networks. We then show that several deterministic models previously developed fall within this framework and we introduce novel network automata following the Cannistraci-Hebb rule. For the theory of realization, we present how to build adaptive network automata for link prediction, which incorporate multiple deterministic models of self-organization and automatically choose the rule that better explains the patterns of connectivity in the network under investigation. We compare Cannistraci-Hebb adaptive (CHA) network automaton against state-of-the-art link prediction methods such as structural perturbation method (SPM), stochastic block models (SBM) and artificial intelligence algorithms for graph embedding. CHA displays an overall higher link prediction performance across different evaluation frameworks on 1386 networks. Finally, we highlight that CHA offers the key advantage to explicitly explain the mechanistic rule of self-organization which leads to the link prediction performance, whereas SPM and graph embedding not. In comparison to CHA, SBM unfortunately shows irrelevant and unsatisfactory performance demonstrating that SBM modelling is not adequate for link prediction in real networks.

scholarly journals Candidate gene prioritization using graph embedding

2020 ◽  
Author(s):  
Quan Do ◽  
Pierre Larmande
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Knowledge Graph ◽  
Learning Techniques ◽  
Number Of Genes ◽  
Important Amount ◽  
Candidate Gene Prioritization ◽  
Gene Information ◽  
Embedding Methods

AbstractCandidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.

Link prediction based on local weighted paths for complex networks

2017 ◽  
Vol 28 (04) ◽  
pp. 1750053
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Rongjing Hu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Structural Similarity ◽  
Prediction Performance ◽  
Topological Feature ◽  
Topological Features ◽  
Node Similarity ◽  
Weighted Paths ◽  
Path Dependent

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

scholarly journals An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

2021 ◽  
Vol 11 (5) ◽  
pp. 1-18
Author(s):  
Md Kamrul Islam ◽  
Sabeur Aridhi ◽  
Malika Smail-Tabbone
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Black Box ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Good Prediction ◽  
Good Prediction Performance ◽  
Node Similarity ◽  
Category Comparison ◽  
Selection Of

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

scholarly journals Survey on graph embeddings and their applications to machine learning problems on graphs

2021 ◽  
Vol 7 ◽  
pp. e357
Author(s):  
Ilya Makarov ◽  
Dmitrii Kiselev ◽  
Nikita Nikitinsky ◽  
Lovro Subelj
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Graph Embedding ◽  
Learning Problems ◽  
Feature Engineering ◽  
Graph Embeddings ◽  
Feature Spaces ◽  
Wide Range ◽  
Depth Analysis ◽  
Node Classification

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

scholarly journals An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

2021 ◽  
Vol 11 (05) ◽  
pp. 1-18
Author(s):  
Md Kamrul Islam ◽  
Sabeur Aridhi ◽  
Malika Smail-Tabbone
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Black Box ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Good Prediction ◽  
Good Prediction Performance ◽  
Node Similarity ◽  
Category Comparison ◽  
Selection Of

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)- based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

A new method for predicting future links in temporal networks based on node influence

2021 ◽  
pp. 2150160
Author(s):  
Cong Li ◽  
Xinsheng Ji ◽  
Shuxin Liu ◽  
Haitao Li
Keyword(s):  
Link Prediction ◽  
Statistical Physics ◽  
Network Science ◽  
Similarity Index ◽  
Prediction Method ◽  
Temporal Networks ◽  
Wide Range ◽  
Ranking Score ◽  
Matching Degree ◽  
Definition Of

Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on [Formula: see text]-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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