scholarly journals GAEN: Graph Attention Evolving Networks

2021 ◽  
Author(s):  
Min Shi ◽  
Yu Huang ◽  
Xingquan Zhu ◽  
Yufei Tang ◽  
Yuan Zhuang ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Real World ◽  
Dynamic Properties ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Representation Learning ◽  
Temporal Networks ◽  
Network Nodes ◽  
Evolving Networks ◽  
Variation Patterns

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.

scholarly journals Network Embedding under Partial Monitoring for Evolving Networks

2019 ◽  
Author(s):  
Yu Han ◽  
Jie Tang ◽  
Qian Chen
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Actual Situation ◽  
Challenging Problem ◽  
Network Embedding ◽  
Strong Assumption ◽  
Evolving Networks ◽  
Real World Datasets ◽  
The Web

Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

scholarly journals Towards Robust Dynamic Network Embedding

2021 ◽  
Author(s):  
Chengbin Hou ◽  
Ke Tang
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Embedding ◽  
Dynamic Nature ◽  
Unique Character

Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be defined as the average number of the changed edges between consecutive snapshots spanning a dynamic network. The degree of changes could be quite different even for the dynamic networks generated from the same dataset. It is natural to ask whether existing DNE methods are effective and robust w.r.t. the degree of changes. Towards robust DNE, we suggest two important scenarios. One is to investigate the robustness w.r.t. different slicing settings that are used to generate different dynamic networks with different degree of changes, while another focuses more on the robustness w.r.t. different number of changed edges over timesteps.

scholarly journals Deconstructing and Reconstructing Resilience: A Dynamic Network Approach

2019 ◽  
Vol 14 (5) ◽  
pp. 765-777 ◽  
Author(s):  
Raffael Kalisch ◽  
Angélique O. J. Cramer ◽  
Harald Binder ◽  
Jessica Fritz ◽  
IJsbrand Leertouwer ◽  
...  
Keyword(s):  
Mental Health ◽  
Complex Dynamics ◽  
Expression Patterns ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Nodes ◽  
Mental Problems ◽  
The Face ◽  
Resilience Factors ◽  
Personality Construct

Resilience is still often viewed as a unitary personality construct that, as a kind of antinosological entity, protects individuals against stress-related mental problems. However, increasing evidence indicates that maintaining mental health in the face of adversity results from complex and dynamic processes of adaptation to stressors that involve the activation of several separable protective factors. Such resilience factors can reside at biological, psychological, and social levels and may include stable predispositions (such as genotype or personality traits) and malleable properties, skills, capacities, or external circumstances (such as gene-expression patterns, emotion-regulation abilities, appraisal styles, or social support). We abandon the notion of resilience as an entity here. Starting from a conceptualization of psychiatric disorders as dynamic networks of interacting symptoms that may be driven by stressors into stable maladaptive states of disease, we deconstruct the maintenance of mental health during stressor exposure into time-variant dampening influences of resilience factors onto these symptom networks. Resilience factors are separate additional network nodes that weaken symptom–symptom interconnections or symptom autoconnections, thereby preventing maladaptive system transitions. We argue that these hybrid symptom-and-resilience-factor networks provide a promising new way of unraveling the complex dynamics of mental health.

IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity

2020 ◽  
Vol 31 (07) ◽  
pp. 2050094
Author(s):  
Xing Su ◽  
Jianjun Cheng ◽  
Haijuan Yang ◽  
Mingwei Leng ◽  
Wenbo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Detection Method ◽  
Dynamic Networks ◽  
Second Phase ◽  
World Systems ◽  
Evolving Networks ◽  
The Third ◽  
Third Phase ◽  
Node Similarity ◽  
Over Time

Many real-world systems can be abstracted as networks. As those systems always change dynamically in nature, the corresponding networks also evolve over time in general, and detecting communities from such time-evolving networks has become a critical task. In this paper, we propose an incremental detection method, which can stably detect high-quality community structures from time-evolving networks. When the network evolves from the previous snapshot to the current one, the proposed method only considers the community affiliations of partial nodes efficiently, which are either newborn nodes or some active nodes from the previous snapshot. Thus, the first phase of our method is determining active nodes that should be reassigned due to the change of their community affiliations in the evolution. Then, we construct subgraphs for these nodes to obtain the preliminary communities in the second phase. Finally, the final result can be obtained through optimizing the primary communities in the third phase. To test its performance, extensive experiments are conducted on both some synthetic networks and some real-world dynamic networks, the results show that our method can detect satisfactory community structure from each of snapshot graphs efficiently and steadily, and outperforms the competitors significantly.

scholarly journals TensorCast: Forecasting Time-Evolving Networks with Contextual Information

2018 ◽  
Author(s):  
Miguel Araújo ◽  
Pedro Ribeiro ◽  
Christos Faloutsos
Keyword(s):  
Social Network ◽  
Real World ◽  
Large Scale ◽  
Information Sources ◽  
Contextual Information ◽  
Temporal Networks ◽  
Evolving Networks ◽  
Award Winning ◽  
Data Source

Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TensorCast, an award winning method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TensorCast is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.

scholarly journals Distributed Queuing in Dynamic Networks

2015 ◽  
Vol 25 (02) ◽  
pp. 1550005 ◽  
Author(s):  
Gokarna Sharma ◽  
Costas Busch
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Connected Graphs ◽  
Network Nodes ◽  
Dynamic Network Model ◽  
Topology Changes ◽  
Communication Links ◽  
Minimum Number ◽  
Distributed Queue ◽  
Distributed Queueing

We consider the problem of forming a distributed queue in the synchronous dynamic network model of Kuhn, Lynch, and Oshman (STOC 2010) in which the network topology changes from round to round but the network stays connected. Queue requests may arrive over rounds at network nodes and the goal is to eventually enqueue them in a distributed queue. We show that in 1-interval connected graphs, where the communication links change arbitrarily between every round, it is possible to solve the distributed queueing problem in [Formula: see text] rounds using [Formula: see text] size messages, where [Formula: see text] is the number of nodes in the network and [Formula: see text] is the number of queue requests. Further, we show that for more stable graphs, e.g. [Formula: see text]-interval connected graphs where the communication links change in every [Formula: see text] rounds, the distributed queuing problem can be solved in [Formula: see text] rounds using the same [Formula: see text] size messages, where [Formula: see text] is the concurrency level parameter that captures the minimum number of active queue requests in the system at any round. These results hold in any arbitrary arrival of queue requests and ensure correctness of the queue formed. To our best knowledge, these are the first solutions to the distributed queuing problem in highly dynamic networks.

Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks

2022 ◽  
Vol 40 (3) ◽  
pp. 1-21
Author(s):  
Lili Wang ◽  
Chenghan Huang ◽  
Ying Lu ◽  
Weicheng Ma ◽  
Ruibo Liu ◽  
...  
Keyword(s):  
Real World ◽  
User Behavior ◽  
Dynamic Networks ◽  
General Purpose ◽  
Local Context ◽  
Structural Role ◽  
Time Dynamic ◽  
Evolving Networks ◽  
Structural Identity ◽  
Novel Method

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.

scholarly journals Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

2021 ◽  
Author(s):  
Jiaxu Liu ◽  
Yingxia Shao ◽  
Sen Su
Keyword(s):  
Community Detection ◽  
Real World ◽  
Local Community ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
High Quality ◽  
Seed Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
High Quality Seed

AbstractLocal community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world networks.

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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