Large Scale Evolving Graphs with Burst Detection

Zhao, Yifeng; Wang, Xiangwei; Yang, Hongxia; Song, Le; Tang, Jie

doi:10.24963/ijcai.2019/613

Cited by 18 publications

(16 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Large Scale Evolving Graphs, on the other hand, where edges and vertices are appended to the network as they arrive in time, sometimes contain bursty links. These links which aim characterize anomalous objects and relationships [8]. Graph structure can also be used in adversarial training [9].…”

Section: Introductionmentioning

confidence: 99%

Graph Motif Entropy for Understanding Time-Evolving Networks

Chen

Bai

Wang

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

The structure of networks can be efficiently represented using motifs, which are those subgraphs that recur most frequently. One route to understanding the motif structure of a network is to study the distribution of subgraphs using statistical mechanics. In this paper, we address the use of motifs as network primitives using the cluster expansion from statistical physics. By mapping the network motifs to clusters in the gas model, we derive the partition function for a network and this allows us to calculate global thermodynamic quantities, such as energy and entropy. We present analytical expressions for the numbers of certain types of motifs, and compute their associated entropy. We conduct numerical experiments for synthetic and real-world data-sets and evaluate the qualitative and quantitative characterizations of the motif entropy derived from the partition function. We find that the motif entropy for real-world networks, such as financial stock market networks, is sensitive to the variance in network structure. This is in line with recent evidence that network motifs can be regarded as basic elements with well defined information-processing functions.

show abstract

Section: Introductionmentioning

confidence: 99%

Graph Motif Entropy for Understanding Time-Evolving Networks

Chen

Bai

Wang

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…Most related works [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] use the discrete model to represent the dynamic networks. Intuitively, they see a dynamic network as a sequence of network snapshots, namely, independent and complete graphs.…”

Section: Discrete Modelmentioning

confidence: 99%

“…In addition, it should be noted that the exactly used network model could be slightly different between each approach. For example, some methods consider network with attributes [15,28,18,12,21,29,31,32,23,34,24,33,26] while others [19,16,27,30,22,20,25] only deal with a raw network. What's more, as we summarized in table 1, there are some methods that can only tackle with adding nodes in the evolution while leave the deletion of nodes in future development.…”

Section: Definition 3 (Continuous Model)mentioning

confidence: 99%

Dynamic Network Embedding Survey

Xue¹,

Zhong²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.

show abstract

“…In other words, there is an implicit regular pattern in users' behavior and users' actions will be carried out subconsciously according to this regular pattern [33]. There have been many researches utilize the behavior logic of users, such idea is widely used in text-based [28] and geographic location based [34] and other fields of recommendation systems [36], after capturing the behavior features of users, we can infer other features of users, such as age, gender, etc. [18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

Seng

Lai

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In the existing social recommendation algorithms, the performance of the algorithms is bothered by the problem of noise and sparsity in social information. In the past few years, researchers have usually focused on solving noise problem or sparse problem, but ignored a key problem, that is if only solving the noise problem, the sparse problem will become more serious, similarly, if only solving the sparse problem, the noise problem will become more serious. Recently, a few studies attempt to solve both noise and sparse problems, usually using explicit trust features to filter trust information and generate implicit trust friends, but this will depend heavily on the quality of explicit trust features. Therefore, in this paper, we propose an adaptive algorithm based on graph convolution network, The main idea is to learn users' implicit trust behavior adaptively. Briefly, we use Jaccard similarity coefficient to filter out unnecessary trust information, the reliable trust information will be the users' trust attributes, then put into the graph convolution network, because of the good feature extraction ability of graph convolution network, we can use adaptive network to learn implicit trust features based on users' features, the trust features can infer the trust attributes for all users. In order to achieve the best effect of learning, we use iterative repetition of the above process, to mining the implicit influence of user trust behavior, so as to alleviate noise and sparsity problems. Experimental result shows that, on three open datasets: LastFM, Douban and Gowalla, the recommendation performance is improved, which proves the effectiveness of the algorithm.

show abstract

Large Scale Evolving Graphs with Burst Detection

Cited by 18 publications

References 23 publications

Graph Motif Entropy for Understanding Time-Evolving Networks

Graph Motif Entropy for Understanding Time-Evolving Networks

Dynamic Network Embedding Survey

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

Contact Info

Product

Resources

About