scholarly journals Susceptible-infected-spreading-based network embedding in static and temporal networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiu-Xiu Zhan ◽  
Ziyu Li ◽  
Naoki Masuda ◽  
Petter Holme ◽  
Huijuan Wang
Keyword(s):  
Random Walk ◽  
Link Prediction ◽  
Large Scale ◽  
Language Model ◽  
Network Evolution ◽  
Temporal Networks ◽  
Network Embedding ◽  
Comparison Task ◽  
Missing Link ◽  
Sample Paths

Abstract Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two susceptible-infected-spreading-based algorithms, i.e., Susceptible-Infected Network Embedding (SINE) on static networks and Temporal Susceptible-Infected Network Embedding (TSINE) on temporal networks. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

scholarly journals Degree-biased random walk for large-scale network embedding

2019 ◽  
Vol 100 ◽  
pp. 198-209 ◽  
Author(s):  
Yunyi Zhang ◽  
Zhan Shi ◽  
Dan Feng ◽  
Xiu-Xiu Zhan
Keyword(s):  
Random Walk ◽  
Large Scale ◽  
Network Embedding ◽  
Biased Random Walk ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Deep Context: A Neural Language Model for Large-scale Networked Documents

2017 ◽  
Author(s):  
Hao Wu ◽  
Kristina Lerman
Keyword(s):  
Word Order ◽  
Link Prediction ◽  
Large Scale ◽  
Language Model ◽  
Distributed Representations ◽  
Large Scale Data ◽  
Context Vector ◽  
Effectiveness And Efficiency ◽  
Data Collections ◽  
Scale Data

We propose a scalable neural language model that leverages the links between documents to learn the deep context of documents. Our model, Deep Context Vector, takes advantage of distributed representations to exploit the word order in document sentences, as well as the semantic connections among linked documents in a document network. We evaluate our model on large-scale data collections that include Wikipedia pages, and scientific and legal citations networks. We demonstrate its effectiveness and efficiency on document classification and link prediction tasks.

scholarly journals Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

2021 ◽  
Vol 2082 (1) ◽  
pp. 012011
Author(s):  
Xiang Xiao ◽  
Kang Zhang ◽  
Shuang Qiu ◽  
Wei Liu
Keyword(s):  
Random Walk ◽  
High Order ◽  
Edge Weight ◽  
Task Graph ◽  
Network Embedding ◽  
Convolutional Network ◽  
Aggregation Model ◽  
Sample Paths ◽  
Attributed Network ◽  
Walk Algorithm

Abstract Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.

scholarly journals Motif-Preserving Temporal Network Embedding

2020 ◽  
Author(s):  
Hong Huang ◽  
Zixuan Fang ◽  
Xiao Wang ◽  
Youshan Miao ◽  
Hai Jin
Keyword(s):  
Link Prediction ◽  
Temporal Dynamics ◽  
Dimensional Space ◽  
Temporal Network ◽  
Hawkes Process ◽  
Temporal Networks ◽  
Network Embedding ◽  
Different Types ◽  
Low Dimensional ◽  
Link Recommendation

Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation.

scholarly journals Microblog Sentiment Classification via Recurrent Random Walk Network Learning

2017 ◽  
Author(s):  
Zhou Zhao ◽  
Hanqing Lu ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
Random Walk ◽  
Social Relations ◽  
Stock Price ◽  
Large Scale ◽  
Sentiment Classification ◽  
Network Embedding ◽  
Network Learning ◽  
Stock Price Prediction ◽  
Recurrent Random Walk ◽  
Public Datasets

Microblog Sentiment Classification (MSC) is a challenging task in microblog mining, arising in many applications such as stock price prediction and crisis management. Currently, most of the existing approaches learn the user sentiment model from their posted tweets in microblogs, which suffer from the insufficiency of discriminative tweet representation. In this paper, we consider the problem of microblog sentiment classification from the viewpoint of heterogeneous MSC network embedding. We propose a novel recurrent random walk network learning framework for the problem by exploiting both users’ posted tweets and their social relations in microblogs. We then introduce the deep recurrent neural networks with random-walk layer for heterogeneous MSC network embedding, which can be trained end-to-end from the scratch. Weemploytheback-propagationmethodfortraining the proposed recurrent random walk network model. The extensive experiments on the large-scale public datasets from Twitter show that our method achieves better performance than other state-of-the-art solutions to the problem.

scholarly journals Supervised temporal link prediction in large-scale real-world networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gerrit Jan de Bruin ◽  
Cor J. Veenman ◽  
H. Jaap van den Herik ◽  
Frank W. Takes
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Systematic Investigation ◽  
Prediction Performance ◽  
Temporal Information ◽  
Temporal Networks ◽  
Discrete Events ◽  
New Approach ◽  
Topological Features

AbstractLink prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link prediction. This paper presents a systematic investigation of supervised temporal link prediction on 26 temporal, structurally diverse, real-world networks ranging from thousands to a million nodes and links. We analyse the relation between global structural properties of each network and the obtained temporal link prediction performance, employing a set of well-established topological features commonly used in the link prediction literature. We report on four contributions. First, using temporal information, an improvement of prediction performance is observed. Second, our experiments show that degree disassortative networks perform better in temporal link prediction than assortative networks. Third, we present a new approach to investigate the distinction between networks modelling discrete events and networks modelling persistent relations. Unlike earlier work, our approach utilises information on all past events in a systematic way, resulting in substantially higher link prediction performance. Fourth, we report on the influence of the temporal activity of the node or the edge on the link prediction performance, and show that the performance differs depending on the considered network type. In the studied information networks, temporal information on the node appears most important. The findings in this paper demonstrate how link prediction can effectively be improved in temporal networks, explicitly taking into account the type of connectivity modelled by the temporal edge. More generally, the findings contribute to a better understanding of the mechanisms behind the evolution of networks.

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

2020 ◽  
Vol 34 (04) ◽  
pp. 4091-4098 ◽  
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.

scholarly journals Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tiago Colliri ◽  
Liang Zhao
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Prediction Method ◽  
High Accuracy ◽  
Financial Information ◽  
Prediction Methods ◽  
Temporal Networks ◽  
Public Data ◽  
Financial Crimes ◽  
Prediction Techniques

AbstractIn this paper, we propose a network-based technique to analyze bills-voting data comprising the votes of Brazilian congressmen for a period of 28 years. The voting sessions are initially mapped into static networks, where each node represents a congressman and each edge stands for the similarity of votes between a pair of congressmen. Afterwards, the constructed static networks are converted to temporal networks. Our analyses on the temporal networks capture some of the main political changes happened in Brazil during the period of time under consideration. Moreover, we find out that the bills-voting networks can be used to identify convicted politicians, who commit corruption or other financial crimes. Therefore, we propose two conviction prediction methods, one is based on the highest weighted convicted neighbor and the other is based on link prediction techniques. It is a surprise to us that the high accuracy (up to 90% by the link prediction method) on predicting convictions is achieved only through bills-voting data, without taking into account any financial information beforehand. Such a feature makes possible to monitor congressmen just by considering their legal public activities. In this way, our work contributes to the large scale public data study using complex networks.

scholarly journals Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk

2020 ◽  
Vol 34 (04) ◽  
pp. 4973-4980
Author(s):  
Zhining Liu ◽  
Dawei Zhou ◽  
Yada Zhu ◽  
Jinjie Gu ◽  
Jingrui He
Keyword(s):  
Random Walk ◽  
Large Scale ◽  
Dimensional Space ◽  
Novelty Detection ◽  
Sampling Strategy ◽  
Temporal Network ◽  
Temporal Networks ◽  
Fine Grained ◽  
Network Representation ◽  
Reinforced Random Walk

Encoding a large-scale network into a low-dimensional space is a fundamental step for various network analytic problems, such as node classification, link prediction, community detection, etc. Existing methods focus on learning the network representation from either the static graphs or time-aggregated graphs (e.g., time-evolving graphs). However, many real systems are not static or time-aggregated as the nodes and edges are timestamped and dynamically changing over time. For examples, in anti-money laundering analysis, cycles formed with time-ordered transactions might be red flags in online transaction networks; in novelty detection, a star-shaped structure appearing in a short burst might be an underlying hot topic in social networks. Existing embedding models might not be able to well preserve such fine-grained network dynamics due to the incapability of dealing with continuous-time and the negligence of fine-grained interactions. To bridge this gap, in this paper, we propose a fine-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. In particular, we start from the notion of fine-grained temporal networks, where the temporal network can be represented as a series of timestamped nodes and edges. Then, we propose the time-reinforced random walk (TRRW) with a bi-level context sampling strategy to explore the essential structures and temporal contexts in temporal networks. Extensive experimental results on real graphs demonstrate the efficacy of our FiGTNE framework.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

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