Topological Information Of Network Research Articles

Extending adamic adar for cold-start problem in link prediction based on network metrics

The cold-start problem is a condition for a new node to join a network with no available information or an isolated node. Most studies use topological network information with the Triadic Closure principles to predict links in future networks. However, the method based on the Triadic Closure principles cannot predict the future link due to no common neighbors between the predicted node pairs. Adamic Adar is one of the methods based on the Triadic Closure principles. This paper proposes three methods for extending Adamic Adar based on network metrics. The main objective is to utilize the network metrics to attract the isolated node or new node to make new relationships in the future network. The proposed method is called the extended Adamic Adar index based on Degree Centrality (DCAA), Closeness Centrality (CloCAA), and Clustering Coefficient (CluCAA). Experiments were conducted by sampling 10% of the dataset as testing data. The proposed method is examined using the four real-world networks by comparing the AUC score. Finally, the experiment results show that the DCAA and CloCAA can predict up to 99% of node pairs with a cold-start problem. DCAA and CloCAA outperform the benchmark, with an AUC score of up to 0,960. This finding shows that the extended Adamic Adar index can overcome prediction failures on node pairs with cold-start problems. In addition, prediction performance is also improved compared to the original Adamic Adar. The experiment results are promising for future research due to successfully improving the prediction performance and overcoming the cold-start problem.

Network Representation Learning Algorithm Based on Complete Subgraph Folding

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph clustering. Existing algorithms commonly ignore the global topological information of the network in network representation learning, leading to information loss. The complete subgraph in the network commonly has a community structure, or it is the component module of the community structure. We believe that the structure of the community serves as the revealed structure in the topology of the network and preserves global information. In this paper, we propose SF-NRL, a network representation learning algorithm based on complete subgraph folding. The algorithm preserves the global topological information of the original network completely, by finding complete subgraphs in the original network and folding them into the super nodes. We employ the network representation learning algorithm to study the node embeddings on the folded network, and then merge the embeddings of the folded network with those of the original network to obtain the final node embeddings. Experiments performed on four real-world networks prove the effectiveness of the SF-NRL algorithm. The proposed algorithm outperforms the baselines in evaluation metrics on community detection and multi-label classification tasks. The proposed algorithm can effectively generalize the global information of the network and provides excellent classification performance.

Topology-informed information dynamics modeling in cyber–physical–social system networks

AbstractCyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

Leader-Based Community Detection Algorithm in Attributed Networks

The community structure plays an indispensable role in developing the deep structure of complex networks. In recent years, some researchers have realized the importance of leader nodes in the community detection process. However, most of the existing leader-based algorithms only use the topological information of networks or attribute information to supplement the topological information, resulting in a significant loss of information integrity. In this paper, we propose a leader-based method that combines topological and attribute information (TALB), uses attribute information among nodes in the network to construct an attribute similarity matrix, and then combines it with network topological information to establish dependency relationships among nodes. As a result, a dependency tree is formed, and the final result of the community division is obtained. Experiments on synthetic networks and real networks show that our proposed method is more effective and practical than the existing leader-based algorithms.

Identifying critical nodes in temporal networks by network embedding

Critical nodes in temporal networks play more significant role than other nodes on the structure and function of networks. The research on identifying critical nodes in temporal networks has attracted much attention since the real-world systems can be illustrated more accurately by temporal networks than static networks. Considering the topological information of networks, the algorithm MLI based on network embedding and machine learning are proposed in this paper. we convert the critical node identification problem in temporal networks into regression problem by the algorithm. The effectiveness of proposed methods is evaluated by SIR model and compared with well-known existing metrics such as temporal versions of betweenness, closeness, k-shell, degree deviation and dynamics-sensitive centralities in one synthetic and five real temporal networks. Experimental results show that the proposed method outperform these well-known methods in identifying critical nodes under spreading dynamic.

Analysis of Maximal Topologies and Their DoFs in Topological Interference Management

Topological interference management (TIM) can obtain degrees of freedom (DoF) gains with no channel state information at the transmitters (CSIT) except topological information of network in the interference channel. It was shown that TIM achieves symmetric DoF 1/2 when internal conflict does not exist among messages [7]. However, it is difficult to assure whether a specific topology can achieve symmetric DoF 1/2 without scrutinizing internal conflict. It is also hard to derive a specific topology directly from the conventional condition for symmetric DoF 1/2. Even except for the topology achieving symmetric DoF 1/2, topology achieving specific DoF less than 1/2 is not well known. With these problems in mind, we propose a method to derive all maximal topologies directly in TIM, named as alliance construction in K-user interference channel. That is, it is proved that a topology is maximal if and only if it is derived from alliance construction. Further we translate a topology design by alliance construction in the alignment-conflict graph into topology matrix and propose conditions for maximal topology matrix (MTM). Moreover, we propose a generalized alliance construction that derives topologies achieving DoF 1/n for n ≥ 3 by generalizing sub-alliances. A topology matrix can also be used to analyze topologies with DoF 1/n .

TAPAS: Train-Less Accuracy Predictor for Architecture Search

In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated highperformance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.

The Average Failure Probabilities of Random Linear Network Coding

In network coding, for the case that the network topology is unknown completely, random linear network coding has been proposed as an acceptable coding technique. In this paper, we define average failure probability of random linear network coding in order to characterize the performance of random network coding, and then analyze this failure probability for different known topological information of network. We obtain several upper bounds on the failure probabilities, and further show that, for some networks, these upper bounds are tight or asymptotically tight. Moreover, if the more topological information of the network is utilized, the better upper bounds are acquired.

Dynamic iteration may fail for partitioned network simulation

AbstractIn many technical applications, complex systems are composed of subsystems that are modelled independently using a network approach. This co‐modelling ansatz is completed very naturally by a co‐simulation approach based on dynamic iteration, where the respective DAE submodels are solved iteratively. For partitioned electric network simulation we show that this approach may fail. As alternative we discuss overlapping techniques based on topological network information.