Real Network Data Research Articles

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion.

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.

Data Flow Optimization in the Internet of Things

The Internet of Things (IoT) is constituted of an important number of constrained nodes limited in terms of power energy, computation capacity, storage capacity. They produce a considerable amount of data, which increases the data flflow in the network. The ineffificient transmission of data via constrained nodes makes the network unstable, the energy consumption increases rapidly, and the data delay increases strictly. To overcome these limitations, we propose a new approach that allows nodes to select the effificient path to transmit data from source nodes to base stations (BSs) to optimize the data flflow in the constrained network. First, we grouped nodes using a density peaks (DP) clustering algorithm based on the coordinate’s location of these nodes. Second, using the group nodes, the assignment of nodes to BSs that are considered as the collectors of data is performed. Third, the nodes make a dynamic and automated path plan to optimize the data flflow in the constrained network. Simulation results on a real network data set demonstrate that our proposal outperforms the state-of-the-art approaches in terms of the number of hops to achieve the cluster head (CH) node, the data delay, the network lifetime, and the number of the alive nodes.

Community detection with nodal information: Likelihood and its variational approximation

Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information and develop likelihood‐based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.

Operation Method of Energy Storage System Replacing Governor for Frequency Regulation of Synchronous Generator without Reserve

This paper proposes an operation methodology for interfacing synchronous generator and energy storage system (ESS) by replacing conventional governor. This research introduces energy management scheme with droop control method for frequency regulation in coordination with synchronous generator. For frequency regulation, generator can take advantage of the ESS instead of the governor, with its relatively fast characteristics, leading to less frequent deviation of synchronous generator. For the verification, we simulate a network reduction model from real Korean network data, based on the case studies using PSCAD/EMTDC software.

A Hierarchical Decision-Making Framework in Social Networks for Efficient Disaster Management

One of the major challenges faced by the current society is developing disaster management strategies to minimize the effects of catastrophic events. Disaster planning and strategy development phases of this urgency require larger amounts of cooperation among communities or individuals in society. Social networks have also been playing a crucial role in the establishment of efficient disaster management planning. This article proposes a hierarchical decision-making framework that would assist in analyzing two imperative information flow processes (innovation diffusion and opinion formation) in social networks under the consideration of community detection. The proposed framework was proven to capture the heterogeneity of individuals using cognitive behavior models and evaluate its impact on diffusion speed and opinion convergence. Moreover, the framework demonstrated the evolution of communities based on their inter-and intracommunication. The simulation results with real social network data suggest that the model can aid in establishing an efficient disaster management policy using social sensing and delivery.

An evolutionary game competitive model for increasing accuracy and maximizing influence in social networks

Given the importance of maximizing influence in a social network, studies in this field often seek to find the nodes that have the most influence on the social network if designated as primary seeds. In this study, to reduce the complexity of computation algorithms, the problem is divided into several groups that aim to find a group of influential people among users of a social network. In this paper, a framework is introduced for solving the problem of influence maximization, which is based on the member clustering by the K means method, to improve the classification of network users, the data are weighted and the problem is modeled and analyzed as an evolutionary game. Finally, calculate its evolutionary stable strategy. This framework has been tested on real social network data for Abrar University students and we have achieved results such as increasing classification accuracy, reducing error function and finding a stable strategy in the community.

Signed Network Node Embedding via Dual Attention Mechanism

In signed networks, GNNs are used to get node embedding by aggregating node neighbor information. Most of the existing methods aggregate neighbor information from the node level, and the different paths between nodes and neighbors will also affect node embedding. The target node and its neighbors have different link positive,negative signs and link directions, which together constitute different paths.These different paths have different contributions to the target node.Based on the structural balance theory and status theory, this paper divides the different paths between nodes and their neighbors into 20 kinds of motifs, which are using to capture the different effects of paths on target nodes. Comprehensive consideration at the node level and path level, SNEDA (Signed Network Embedding via dual attention Mechanism) is proposed based on the graph attention Network. The model has two attention mechanisms: node-level attention captures different influences between nodes at the node level; path-level attention captures the different influences between motifs at the path level. The final vector representation of nodes is obtained by aggregating neighbor information selectively based on important motifs, and the vector representation is applied to link prediction. Experiments on four real social network data sets show that the network representation obtained by the model can improve the accuracy of link prediction. Experimental results demonstrate the effectiveness of the proposed framework through a signed link prediction task on four real-world signed network datasets.

Retracted] Research on Maximizing Influence of Blockchain Social Network Based on BCLT Model

In the blockchain social network, the traditional influence maximization algorithm has the problem of insufficient accuracy of the influence spread. To solve the above problem, a BCLT model including the characteristics of the blockchain is established based on the linear threshold model. The BC‐RIS algorithm is proposed based on the reverse reachable set. The BC‐RIS algorithm's influence spread and running time and the traditional algorithm is compared using the real blockchain social network data set. The experimental results show that the BC‐RIS algorithm can obtain a larger influence spread range, which is more in line with the influence propagation law of the blockchain social network.

Online Change Point Detection for Weighted and Directed Random Dot Product Graphs

Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence. We characterize the distribution of this running statistic to select thresholds that guarantee error-rate control, and under simplifying approximations we offer insights on the algorithm’s detection resolution and delay. The end result is a lightweight online CPD algorithm, that is also explainable by virtue of the well-appreciated interpretability of RDPG embeddings. This is in stark contrast with most existing graph CPD approaches, which either rely on extensive computation, or they store and process the entire observed time series. An apparent limitation of the RDPG model is its suitability for undirected and unweighted graphs only, a gap we aim to close here to broaden the scope of the CPD framework. Unlike previous proposals, our non-parametric RDPG model for weighted graphs does not require a priori specification of the weights’ distribution to perform inference and estimation. This network modeling contribution is of independent interest beyond CPD. We offer an open-source implementation of the novel online CPD algorithm for weighted and direct graphs, whose effectiveness and efficiency are demonstrated via (reproducible) synthetic and real network data experiments.

Multi-step locally expansion detection method using dispersed seeds for overlapping community

The local expansion method is a novel and promising community detection algorithm. Just based on part of network information, it can detect overlapping communities effectively, but some problems exist such as seed node aggregation, poor quality and inaccurate community coverage. Therefore, we propose a local expansion overlapping community detection algorithm based on dispersed seeds. There are four essential parts of this algorithm: 1) We firstly generate non-overlapping partitions of the network, and locate seed nodes with the largest influence in their own partition by using a new index of node influence, which combines the information centrality of nodes and the number of k-order neighbors. 2) Secondly, on the condition of the neighborhood overlap measure maximization, seed nodes merge unseeded nodes to generate a preliminary seed community; 3) Then based on the community conductance gain, the allocated nodes are screened and the free nodes are assigned to the seed community; 4) In the end, a node-community similarity based on common connection edge is proposed to re-allocate new free nodes and obtain the final community structure. This method can make the community distribution more proper and the coverage more reasonable. The experimental results on some artificial data and real network data show that the algorithm performs well on overlapping community indicators such as EQ and ONMI, while the community detection results are more stable.

Anomaly-based Network Intrusion Detection using Ensemble Machine Learning Approach

In this study, an Intrusion Detection System (IDS) is designed based on Machine Learning classifiers, and its performance is evaluated for the set of attacks entailed in the UNSW- NB15 dataset. UNSW- NB15 dataset contains 2,540,226 realistic network data instances and 49 features. Most research uses a representative sample of this dataset with present training and testing subsets, which includes 257,673 records in total. The dataset was submitted to visual data analysis to discover poten-tial reasons or flaws which likely challenge Machine Learning classifiers. Pre-processing strategies are necessary before this data can be used for data-driven prototype development for IDS because of the class representation imbalance with pattern counts and feature overlap. The method used for pre-processing is implemented by min-max scaling in the normalization phase, followed by applying Elastic Net and Sequential Feature Selection (SFS) algorithms. This work employed ensemble methods using three base classifiers, namely Balanced Bagging, XGBoost, and RF-HDDT, augmented to address the imbalance issue. Parameters of Balanced Bagging and XGBoost are tuned for the imbalanced data, and the Hellinger distance metric supplements random Forest to address the limitations of the default distance metric. Two new algorithms are proposed to address the class overlap issue in the dataset and applied during training. These two algorithms are leveraged to help improve the performance on the testing dataset by affecting the final classification decision made by three base classifiers as part of the ensemble classifier, which employs a majority vote combiner. The performance evaluation of the proposed method for binary and multi-category classification was evaluated using standard metrics, including those generated from the confusion matrix, and compared to other studies using the same dataset. The proposed design outperforms those reported in the literature by a significant margin for binary and multi-category classification cases.

Spectral detection of simplicial communities via Hodge Laplacians.

While the study of graphs has been very popular, simplicial complexes are relatively new in the network science community. Despite being a source of rich information, graphs are limited to pairwise interactions. However, several real-world networks such as social networks, neuronal networks, etc., involve interactions between more than two nodes. Simplicial complexes provide a powerful mathematical framework to model such higher-order interactions. It is well known that the spectrum of the graph Laplacian is indicative of community structure, and this relation is exploited by spectral clustering algorithms. Here we propose that the spectrum of the Hodge Laplacian, a higher-order Laplacian defined on simplicial complexes, encodes simplicial communities. We formulate an algorithm to extract simplicial communities (of arbitrary dimension). We apply this algorithm to simplicial complex benchmarks and to real higher-order network data including social networks and networks extracted using language or text processing tools. However, datasets of simplicial complexes are scarce, and for the vast majority of datasets that may involve higher-order interactions, only the set of pairwise interactions are available. Hence, we use known properties of the data to infer the most likely higher-order interactions. In other words, we introduce an inference method to predict the most likely simplicial complex given the community structure of its network skeleton. This method identifies as most likely the higher-order interactions inducing simplicial communities that maximize the adjusted mutual information measured with respect to ground-truth community structure. Finally, we consider higher-order networks constructed through thresholding the edge weights of collaboration networks (encoding only pairwise interactions) and provide an example of persistent simplicial communities that are sustained over a wide range of the threshold.

Detecting Bots and Assessing Their Impact in Social Networks

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.

Planned Special Event Network Optimization Model Considering Parking and Ridesharing Drop-Off

A planned special event (PSE), such as a sports game or a concert, can greatly affect the normal operations of a transportation system. To facilitate traffic, the road network is usually reconfigured, which could include road closures, reversed lanes, and limited access to parking facilities. For recurring PSEs, event-goers are often provided with recommended routes to designated parking areas in advance. Such network reconfiguration and route and parking recommendations are, however, often ad hoc in practice. This paper focuses on the PSE traffic planning problem. We propose to simultaneously consider parking, ridesharing, and network configuration. The problem is formulated as an optimization problem with integer decision variables. We developed a flow-based traffic simulation tool that is able to incorporate parking and lane changing (which cannot be ignored around ridesharing drop-off locations) to evaluate the objective function. We also developed effective and efficient heuristic solution algorithms. The models and algorithms are tested using the real network and traffic data from Super Bowl XLIX in 2015. The results show that our methods and approaches are able to produce an effective comprehensive traffic plan with reasonable computation time. For the Super Bowl XLIX case study, the resulting optimal plan is able to save 39.6% of the total vehicle-hours associated with default network configurations. Sensitivity analysis has also been conducted with respect to the compliance rate of travelers following recommended routes. It is found that the resulting near-optimal PSE traffic plans are able to tolerate some uncertainty in the compliance rate.

Graph Matching between Bipartite and Unipartite Networks: to Collapse, or not to Collapse, that is the Question.

Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements. In this paper we address the common setting in which one of the graphs to match is a bipartite network and one is unipartite. Commonly, the bipartite networks are collapsed or projected into a unipartite graph, and graph matching proceeds as in the classical setting. This potentially leads to noisy edge estimates and loss of information. We formulate the graph matching problem between a bipartite and a unipartite graph using an undirected graphical model, and introduce methods to find the alignment with this model without collapsing. We theoretically demonstrate that our methodology is consistent, and provide non-asymptotic conditions that ensure exact recovery of the matching solution. In simulations and real data examples, we show how our methods can result in a more accurate matching than the naive approach of transforming the bipartite networks into unipartite, and we demonstrate the performance gains achieved by our method in simulated and real data networks, including a co-authorship-citation network pair, and brain structural and functional data.

Leakage Localization in District Heating Networks Based on Real Network and Measurement Data

Fast localization of leakages in district heating networks is a demanding yet important task for network operation. Every leakage results in a loss of medium thus of energy. In case of small leakages, the medium lost can be replenished to maintain network operation. However, if the loss of medium is too large, it may be inevitable to shut down network operation. This can be prevented by separating the damaged network part using exclusion areas. For this, the leakage must be assigned to the correct exclusion area as fast and accurately as possible. This is a major challenge for current district heating networks without existing or with leakage localization systems delivering results too slow, so that other methods of leakage detection and localization are needed here. In a joint research project, three model- and data-based approaches are developed to localize leakages in real time. These are based on operating data which are typically measured for operational needs such as pressure or flow rate. The first approach evaluates the pressure wave which traverses the network at speed of sound when a leakage occurs. The second, purely data-driven approach uses machine learning models to localize leakages. The training phase of this approach is based on a large number of simulated leakages using a network model. The last approach uses the same network model to numerically compute the network state and localize leakages depending on available measurement data. In previous publications these approaches are evaluated using simulated measurement data with encouraging results. In this paper the work is continued and carried out to measurement data of a real district heating network. It is shown that each method is individually applicable to localize leakages under real conditions and provides plausible results. This encourages further development for using synergies which may deliver even better results.

PageRank centrality and algorithms for weighted, directed networks

PageRank (PR) is a fundamental tool for assessing the relative importance of the nodes in a network. In this paper, we propose a measure, weighted PageRank (WPR), extended from the classical PR for weighted, directed networks with possible non-uniform node-specific information that is dependent or independent of network structure. A tuning parameter leveraging node degree and strength is introduced. An efficient algorithm based on R program has been developed for computing WPR in large-scale networks. We have tested the proposed WPR on widely used simulated network models, and found it outperformed the classical PR. Additionally, we apply the proposed WPR to the real network data generated from World Input–Output Tables as an example, and have seen the results that are consistent with the global economic trends, which renders it a preferred measure in the analysis.

The topological Dirac equation of networks and simplicial complexes

We define the topological Dirac equation describing the evolution of a topological wave function on networks or on simplicial complexes. On networks, the topological wave function describes the dynamics of topological signals or cochains, i.e. dynamical signals defined both on nodes and on links. On simplicial complexes the wave function is also defined on higher-dimensional simplices. Therefore the topological wave function satisfies a relaxed condition of locality as it acquires the same value along simplices of dimension larger than zero. The topological Dirac equation defines eigenstates whose dispersion relation is determined by the spectral properties of the Dirac operator defined on networks and generalized network structures including simplicial complexes and multiplex networks. On simplicial complexes the Dirac equation leads to multiple energy bands. On multiplex networks the topological Dirac equation can be generalized to distinguish between different mutlilinks leading to a natural definition of rotations of the topological spinor. The topological Dirac equation is here initially formulated on a spatial network or simplicial complex for describing the evolution of the topological wave function in continuous time. This framework is also extended to treat the topological Dirac equation on 1 + d lattices describing a discrete space-time with one temporal dimension and d spatial dimensions with d ∈ {1, 2, 3}. It is found that in this framework space-like and time-like links are only distinguished by the choice of the directional Dirac operator and are otherwise structurally indistinguishable. This work includes also the discussion of numerical results obtained by implementing the topological Dirac equation on simplicial complex models and on real simple and multiplex network data.

Targeted pandemic containment through identifying local contact network bottlenecks.

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts-between individuals or between population centres-are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.