Evolution Of Dynamic Networks Research Articles

Contrastive representation learning on dynamic networks

Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning. This paper proposes a novel Dynamic Network Contrastive representation Learning (DNCL) model. Specifically, contrast objective functions are constructed using intra-snapshot and inter-snapshot contrasts to capture the network topology, node feature information, and network evolution information, respectively. Rather than estimating or generating ground-truth network features, the proposed approach maximizes mutual information between nodes from different time steps and views generated. The experimental results of link prediction, node classification, and clustering on several real-world and synthetic networks demonstrate the superiority of DNCL over state-of-the-art methods, indicating the effectiveness of the proposed approach for dynamic network representation learning.

Social Trading, Communication, and Networks

Social trading is an emerging market in the sharing economy, allowing investors (followers) to duplicate the trades of other investors (leaders) in real time. We analyze the formation and dissolution of links in a large social trading network. Such networks are characterized by the rapid dissolution of links, increasing the importance of studying network dissolution. We investigate how social communication, along with financial performance and demographics, affects dynamic network evolution. We show that different types of social communication, such as posts and comments, have different implications for link formation and dissolution. Moreover, we find financial performance to be highly important for link formation and dissolution, whereas demographic characteristics are only relevant for link formation. In social trading, the extreme flexibility of followers in dissolving links and thereby, terminating their relationship instantaneously brings about large income uncertainty for leaders. Thus, a thorough understanding of network evolution and its determinants is crucial for leaders. Our results can provide guidance on when and how to communicate with followers. As vocal leaders on social media may exert a significant influence on financial markets—as demonstrated by recent the GameStop frenzy—a better understanding of the evolution of investment networks is also important for regulators.

Cascaded, Feedback-Driven, and Spatially Localized Emergence of Constitutional Dynamic Networks Driven by Enzyme-Free Catalytic DNA Circuits.

The enzyme-free catalytic hairpin assembly (CHA) process is introduced as a functional reaction module for guided, high-throughput, emergence, and evolution of constitutional dynamic networks, CDNs, from a set of nucleic acids. The process is applied to assemble networks of variable complexities, functionalities, and spatial confinement, and the systems provide possible mechanistic pathways for the evolution of dynamic networks under prebiotic conditions. Subjecting a set of four or six structurally engineered hairpins to a promoter P1 leads to the CHA-guided emergence of a [2 × 2] CDN or the evolution of a [3 × 3] CDN, respectively. Reacting of a set of branched three-arm DNA-hairpin-functionalized junctions to the promoter strand activates the CHA-induced emergence of a three-dimensional (3D) CDN framework emulating native gene regulatory networks. In addition, activation of a two-layer CHA cascade circuit or a cross-catalytic CHA circuit and cascaded driving feedback-driven evolution of CDNs are demonstrated. Also, subjecting a four-hairpin-modified DNA tetrahedron nanostructure to an auxiliary promoter strand simulates the evolution of a dynamically equilibrated DNA tetrahedron-based CDN that undergoes secondary fueled dynamic reconfiguration. Finally, the effective permeation of DNA tetrahedron structures into cells is utilized to integrate the four-hairpin-functionalized tetrahedron reaction module into cells. The spatially localized miRNA-triggered CHA evolution and reconfiguration of CDNs allowed the logic-gated imaging of intracellular RNAs. Beyond the bioanalytical applications of the systems, the study introduces possible mechanistic pathways for the evolution of functional networks under prebiotic conditions.

HyperDNE: Enhanced hypergraph neural network for dynamic network embedding

Representation learning provides an attractive opportunity to model the evolution of dynamic networks. However, the existing methods have two limitations: (1) most graph neural network-based methods fail to utilize the high-order proximity of nodes that captures the important properties of a network topology; (2) evolutionary dynamics-based methods are much fine-grained in modeling time information but neglect the coherence of dynamic networks, which leads to the model being susceptible to subtle noise. In this paper, we propose an enhanced hypergraph neural network framework for dynamic network embedding (HyperDNE) to tackle these issues. Specifically, we innovatively design a sequential hypergraph with dual-stream output to explore the group properties of nodes and edges, and a line graph neural network is added as an auxiliary enhancement scheme to further aggregate social influence from the degree of social convergence. Then, we compute the final embedding through attentions along the node and hyperedge levels to fuse multi-level variations in the network structure. The experimental results on six real networks demonstrate significant gains for HyperDNE over several state-of-the-art network embedding baselines. The dataset and source code of HyperDNE are publicly available at https://github.com/qhgz2013/HyperDNE.

Mixed Opinion Dynamics Based on DeGroot Model and Hegselmann–Krause Model in Social Networks

Most existing opinion formation processes apply one opinion dynamics model. However, this article combines opinion formation and complex networks to innovatively develop two new opinion dynamics models to more realistically describe the opinion evolution process: 1) an opinion similarity mixed (OSM) model and 2) a structural similarity mixed (SSM) model, both of which include characteristics from the DeGroot model and the Hegselmann–Krause bounded confidence model. In addition, the strong and weak relations between individuals are considered. The network dynamically changes by two developed network updating algorithms based on opinion similarity and structural similarity. Simulations are then conducted using artificial and real-world networks, which are Erdös-Rényi random networks, random regular networks, scale-free networks, and the Twitter network. It is found that compared with static networks, the opinion evolution in dynamic networks produces fewer opinion clusters and smaller opinion variances. The dynamic network mechanism reduces the weak relations between agents and improves the global clustering coefficient in the ER random networks but not in the Twitter network, which means that the network topology has an impact on results. Therefore, it is concluded that agents’ subjective behaviors significantly influence the outcome of opinion evolution and networks, which is consistent with real life.

Core node knowledge based multi-objective particle swarm optimization for dynamic community detection

Community detection aims to classify nodes with similar attributes, and it becomes more difficult when the network structure changes over time. In order to tap into the evolution of dynamic networks, nodes with high criticality in dynamic networks clearly need to be attended. In this paper, a novel dynamic community detection algorithm based on particle swarm optimization is proposed. The core nodes in the network are obtained by calculating the resistance distance of each node. The nodes associated with these core nodes that make up the constant community. The constant community should be retained is the knowledge gained from the evolution of the core nodes in two consecutive time steps. To ensure the quality of community division at the current time step and the smoothness of two consecutive time steps, a new particle updating parameter is proposed, which is based on the optimal individual at the previous time step and the population at the current time step. Compared with the other well-known related algorithms, experimental result on many real-world and synthetic networks have illustrated the higher accuracy and stability of the proposed.

Structural Evolutions of Initially Amorphous Polymers during Near‐Tg Stretching: A Minireview of Recent Progresses

AbstractIndustrial processing of polymeric products often involves stretching from glassy state. Therefore, the understanding of structural evolution of glassy polymers during stretching around glass transition temperatures (Tg) is of fundamental significance to predict suitable processing parameters for industrial purpose. However, former reviews have mostly focused on the structural evolution of polymers being stretched from melt or crystalline state, whereas there still lacks summaries dedicated to stretching studies from the amorphous state. This work summarizes important advances of researches on both non‐crystallizable and crystallizable polymers concerning their stretching behavior near Tg. Based on case studies on typical examples of each kind, the review systematically presented the structural changes including chain orientation/relaxation kinetics, dynamic network evolution, and stretch‐induced phase transition. A discussion about the influence of these structural evolutions on essential end‐use properties of materials is also included, in the hope of providing guidance on future stretching‐related studies.

Evolution of dynamical networks enhances catalysis in a designer enzyme.

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity-temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes.

P-adic numbers encode complex networks

The Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.

Evolution pattern mining on dynamic social network

Recently, due to the popularity of social websites and apps, considerable attention has been paid to the analysis of the structure of social networks. Clearly, social networks usually evolve over time; some new users and relationships are established; and some obsolete ones are removed. This dynamic feature definitely increases the complexity of pattern discovery. In this paper, we introduce a new representation to express the dynamic social network and a new type of pattern, the evolution pattern, to capture the interaction evolutions in a dynamic social network. Furthermore, a novel algorithm, evolution pattern miner (EPMiner), is developed to efficiently discover the evolution characteristics. EPMiner also employs some pruning strategies to effectively reduce the search space to improve the performance. The experimental results on several datasets show the efficiency and the scalability of EPMiner for extracting interaction evolution in dynamic networks. Finally, we apply EPMiner on real datasets to show the practicability of evolution pattern mining.

Temporal Link Prediction with Motifs for Social Networks

Link prediction has attracted considerable attention. Empiricism and the evolution mechanism based approach are the mainstream methods for link prediction. However, one drawback of such approaches is that they usually ignore the dynamic evolution mechanism of social networks, yet being dynamic is an essential characteristic of a social network that exists in every stage of the networks evolution. In this paper, we address the problem of temporal link prediction and investigate social networks from the time dimension with the purpose of dynamic evolution mechanism capturing. First, we separate a temporal network into a series of snapshots. Then, we propose a triad transition matrix prediction algorithm to learn the change of the distribution of triads among the different snapshots. The learned changes in the distribution of triads can capture the dynamic evolution of the network. With a proposed triad transition influence quantification algorithm, we propose a motifs based link prediction method for temporal link prediction. The proposed method can capture the dynamic evolution of temporal networks and is universal than existing methods. Extensive experiments on disparate real-world networks and model networks with controllable evolution demonstrate the effectiveness of the proposed method.

A Boon or a Bane? An Examination of Social Communication in Social Trading

Social trading is an emerging market in the sharing economy, allowing inexperienced investors (copiers) to automatically follow the trades of experts (leaders) in real time. We use a separable temporal exponential random graph model (STERGM) to analyze the link formation and dissolution of a large social trading network. In contrast to traditional social networks, social trading networks are characterized by a rapid dissolution of links, thereby increasing the importance of studying network dissolution. We investigate how social communication, along with financial performance and demographics, affects dynamic network evolution and address the existing dependence among copier-leader links. Our results show that social communication, financial performance, and demographic factors are important determinants of link formation. However, once a link is formed, copiers mainly focus on financial performance and communication but not on demographic factors. Thus, the determinants of link formation and dissolution are asymmetric. Different types of social communication, such as posts and comments, have different implications for link formation and dissolution. Our findings provide important implications for both investors and social trading platforms.

An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

How Memory Conforms to Brain Development.

Nature exhibits countless examples of adaptive networks, whose topology evolves constantly coupled with the activity due to its function. The brain is an illustrative example of a system in which a dynamic complex network develops by the generation and pruning of synaptic contacts between neurons while memories are acquired and consolidated. Here, we consider a recently proposed brain developing model to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes. Following recent experimental observations, we assume that the basic rules for adding and removing synapses depend on local synaptic currents at the respective neurons in addition to global mechanisms depending on the mean connectivity. In this way a feedback loop between “form” and “function” spontaneously emerges that influences the ability of the system to optimally store and retrieve sensory information in patterns of brain activity or memories. In particular, we report here that, as a consequence of such a feedback-loop, oscillations in the activity of the system among the memorized patterns can occur, depending on parameters, reminding mind dynamical processes. Such oscillations have their origin in the destabilization of memory attractors due to the pruning dynamics, which induces a kind of structural disorder or noise in the system at a long-term scale. This constantly modifies the synaptic disorder induced by the interference among the many patterns of activity memorized in the system. Such new intriguing oscillatory behavior is to be associated only to long-term synaptic mechanisms during the network evolution dynamics, and it does not depend on short-term synaptic processes, as assumed in other studies, that are not present in our model.

Modelling dynamic network evolution as a Pitman-Yor process

Dynamic interaction networks frequently arise in biology, communications technology and the social sciences, representing, for example, neuronal connectivity in the brain, internet connections between computers and human interactions within social networks. The evolution and strengthening of the links in such networks can be observed through sequences of connection events occurring between network nodes over time. In some of these applications, the identity and size of the network may be unknown a priori and may change over time. In this article, a model for the evolution of dynamic networks based on the Pitman-Yor process is proposed. This model explicitly admits power-laws in the number of connections on each edge, often present in real world networks, and, for careful choices of the parameters, power-laws for the degree distribution of the nodes. A novel empirical method for the estimation of the hyperparameters of the Pitman-Yor process is proposed, and some necessary corrections for uniform discrete base distributions are carefully addressed. The methodology is tested on synthetic data and in an anomaly detection study on the enterprise computer network of the Los Alamos National Laboratory, and successfully detects connections from a red-team penetration test.

Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings.

Dynamic networks evolve with their structures changing over time. It is still a challenging problem to efficiently explore the evolution of dynamic networks in terms of both their structural and temporal properties. In this paper, we propose a visual analytics methodology to interactively explore the temporal evolution of dynamic networks in the context of their structure. A novel diachronic node embedding method is first proposed to learn latent representations of the structural and temporal features of nodes in a vector space. Diachronic node embeddings are then used to discover communities with similar structural proximity and temporal evolution patterns. A visual analytics system is designed to enable users to visually explore the evolutions of nodes, communities, and the network as a whole in terms of their structural and temporal properties. We evaluate the effectiveness of our method using artificial and real-world dynamic networks and comparisons with previous methods.

Ike Net: Email and Friendship Evolution

Abstract Network evolution is an important problem for social scientists, management consultants, and social network scholars. Unfortunately, few empirical data sets exist that have sufficient data to fully explore evolution dynamics. Increasingly, more and more online data sets are used in lieu of offline, face-to-face data. The veracities of these findings are questionable, however, because there are few studies exploring the similarity of online-offline dynamics. The IkeNet project investigated online and offline network evolution. Empirical data was collected on a group of 22 mid-career military officers going through a one-year graduate program. Data collection included email communication collected from the Exchange server, as well as self-reported friendship, and time spent together, over a course of 20 weeks. Numerous attribute data on the individual actors was collected from their military personnel files. The data allows network scholars to conduct research into the dynamics of network evolution and allows educators a real-world example data set for use in classroom instruction.

Exploring temporal community structure and constant evolutionary pattern hiding in dynamic networks

Abstract Detecting the temporal communities and analyzing their evolution in dynamic networks is an important question for understanding the structure and function of complex networks. Most existing methods deal the temporal community detection and evolution as a two-step processes and only apply to unweighted and undirected dynamic networks. In this paper, we proposed a new clustering method based on non-negative matrix factorization from a fully probabilistic perspective, to explore temporal and constant community structure as well as the importance of nodes in any type dynamic networks synchronously. In detailed, we first denote the important matrix of node in dynamic networks, the community membership matrix, the similarity matrix at each snapshot and the probability transition matrix of community between the two consecutive snapshots. Second, we constitute the temporal community detection method from the view of generating networks. Third, we introduce a gradient descent algorithm to optimize the objection function of the proposed model. The experimental results on both artificial and real dynamic networks demonstrate that the superior performance of our proposed method is over some widely-used methods.

The resistance perturbation distance: A metric for the analysis of dynamic networks

To quantify the fundamental evolution of time-varying networks, and detect abnormal behavior, one needs a notion of temporal difference that captures significant organizational changes between two successive instants. In this work, we propose a family of distances that can be tuned to quantify structural changes occurring on a graph at different scales: from the local scale formed by the neighbors of each vertex, to the largest scale that quantifies the connections between clusters, or communities. Our approach results in the definition of a true distance, and not merely a notion of similarity. We propose fast (linear in the number of edges) randomized algorithms that can quickly compute an approximation to the graph metric. The third contribution involves a fast algorithm to increase the robustness of a network by optimally decreasing the Kirchhoff index. Finally, we conduct several experiments on synthetic graphs and real networks, and we demonstrate that we can detect configurational changes that are directly related to the hidden variables governing the evolution of dynamic networks.

Accelerated Distributed Dual Averaging Over Evolving Networks of Growing Connectivity

We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Further, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.