Spatial-Temporal Attention TCN-Based Link Prediction for Opportunistic Network

The dynamic evolution of networks opportunities is very complex, which brings challenges to link prediction. By analyzing the mobile characteristics of nodes, a new link prediction algorithm of opportunistic networks based on a 3D convolutional neural network (3D-CNN) was proposed. It uses the evolution information of the whole network topology to predict the possible changes of local links. In this paper, network attributes under different dimensions, such as time, space, correlation between nodes, are combined to represent opportunistic networks. The pattern classification method is adopted to predict future link combinations between multi-nodes. Using a 3D convolutional neural network to extract spatiotemporal features from multi-dimensional attributes can affect local link status. According to the extracted features, the evolution trend of future links can be inferred, and the multi-node link prediction of opportunistic networks can be realized. We have designed experiments under different conditions to find the best parameters. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lower-limb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates the spatial-temporal-based, dilation-convolution-based, dyna- mic routing and vector-based features for recognizing locomotion mode recognition with small data rather than big-data-based neural networks for robotic prostheses. TCCN proposed in this study has four characteristics, which extracts the (1) spatial-temporal information in the data and then makes (2) dilated convolution to deal with small data, and uses (3) dynamic routing, which produces some similarities to the human brain to process the data as a (4) vector, which is different from other scalar-based networks, such as convolutional neural network (CNN). By comparison with a traditional machine learning, e.g., support vector machine(SVM) and big-data-driven neural networks, e.g., CNN, recurrent neural network(RNN), temporal convolutional network(TCN) and capsule network(CN). The accuracy of TCCN is 4.1% higher than CNN under 5-fold cross-validation of three-locomotion-mode and 5.2% higher under the 5-fold cross-validation of five-locomotion modes. The main confusion we found appears in the transition state. The results indicate that TCCN may handle small data balancing global and local information which is closer to the way how the human brain works, and the capsule layer allows for better processing vector information and retains not only magnitude information, but also direction information.

Interpretable spatial-temporal attention convolutional network for rainfall forecasting

The target of link prediction is used to estimate the possibility of future links among nodes through known network structures and nodes information. According to the time-varying characteristics of the opportunistic network, the historical information of node pairs has a significant influence on the future connection state. We propose a novel link prediction approach which is based on the recurrent neural network link prediction (RNN-LP) framework. With the help of time series method, we define the vector that is made up of the node information and historical connection information of the node pairs, in which a sequence vector is constructed. Benefiting from RNN in sequence modeling, the time domain characteristics were extracted in the process of the dynamic evolution of the opportunistic network. Hence, the future link prediction becomes significantly better. By utilizing iMote traces Cambridge and MIT reality datasets, experimental results are obtained to reveal that RNN-LP method gives better accuracy and stability than the prediction techniques of the common neighbor, Adamic-Adar, resource allocation, local path, and Katz.

Combining contextual, temporal and topological information for unsupervised link prediction in social networks

Link prediction in complex networks aims to mine hidden or to-be-generated links between network nodes, which plays a significant role in fields such as the cold start of recommendation systems, knowledge graph completion and biomedical experiments. The existing link prediction models based on graph neural networks, such as graph convolution neural networks, often only learn the low-frequency information reflecting the common characteristics of nodes while ignoring the high-frequency information reflecting the differences between nodes when learning node representation, which makes the corresponding link prediction models show over smoothness and poor performance. Focusing on links in complex networks, this paper proposes an edge convolutional graph neural network EdgeConvHiF that fuses high-frequency node information to achieve the representation learning of links so that link prediction can be realized by implementing the classification of links. EdgeConvHiF can also be employed as a baseline, and extensive experiments on real-world benchmarks validate that EdgeConvHiF not only has high stability but also has more advantages than the existing representative baselines.

Multi-view fusion neural network for traffic demand prediction

Accurate load forecasting is essential for ensuring safe, stable, and economical operation of energy internet. Temporal convolutional networks (TCNs) have demonstrated superior performance, when compared to recurrent neural network models, since their introduction in electrical load forecasting. However, the current TCN‐based models are unable to obtain a large receptive field and strong long‐time feature extraction capability owing to the specific kernel size of the 1D convolution structure. This paper proposes a temporal inception convolutional network based on multi‐head attention (TICN‐Att) for ultra‐short‐term load prediction. By introducing an inception structure into the TCN, the proposed model can extract multi‐dimensional information from the input features, through the multiple hidden convolutional kernels of different scales, without stacking layers depth‐wise. Simultaneously, by introducing a multi‐head attention mechanism, the TICN‐Att model has a long time‐dependent extraction capability, similar to that of the long short‐term memory network models. The generalization and validity of the model are tested using the global energy forecasting competition (GEFCOM2014) dataset, electrical load data of a city in Jiangsu (China), and PJM power system dataset. The experimental results demonstrated that the proposed model has the best prediction effect, compared to the other state‐of‐the‐art models.

Link prediction aims to find the missing link in current networks or estimate the likelihood the link will appear in the future. In many real-world scenarios, networks can be massive and drastically evolving. Many recent works concentrated on how to solve the procession of massive data, which cares less about the effects of the temporal information. In some dynamic networks, the edge that appears a long time ago will have fewer effects on the appearance of edges in the future. The accuracy of link prediction in dynamic networks can be further improved by adding temporal information in prediction measures. In this paper, we mainly explore the effects of the link temporal information in the graph stream link prediction. We design a graph stream-based framework to solve link prediction problems in dynamic networks which can provide a convenient platform to implement different link prediction methods for dynamic networks, and evaluate these methods on a fair basis. Then, we proposed the Time Decay-based Link Prediction (TDLP) method to improve the efficient of link prediction problem in the dynamic networks. TDLP extends the neighborhood-based measure with time decay functions in graph stream scenarios which can handle temporal information and massive data. Experiment results demonstrate that the accuracy of link prediction for dynamic networks can be efficiently improved in our TDLP method.

Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks

Graph neural networks (GNNs) have been proved useful for link prediction in online social networks. However, existing GNNs can only adopt shallow architectures as too many layers will lead to over-smoothing and vanishing gradient during training. It causes nodes to have indistinguishable embeddings if locally they have similar structural positions, and this further leads to inaccurate link prediction. In this paper, we propose a unified end-to-end deep learning model, namely Neural Link Prediction (NeuLP), which can integrate the linearity and non-linearity user interactions to overcome the limitation of GNNs. The experimental evaluation demonstrates our model’s significant improvement over several baseline models. Moreover, NeuLP achieves a reliable link prediction given two users’ different types of attributes and it can be applied to other pairwise tasks. We further perform in-depth analyses on the relation between prediction performance and users’ geodesic distance and show that NeuLP still can make accurate link prediction while two users are far apart in the networks.

Accurate and fast prediction of pedestrian trajectory is very important. However, the interaction between pedestrians is complex, pedestrians are affected not only by their own motion but also by the neighboring pedestrians. Some methods do not extract the pedestrian motion features completely. It is easy to ignore a few of important features of pedestrians. The Spatial-Temporal Attention Graph Convolutional Network (ST-AGNN) for pedestrian trajectory prediction is proposed in this paper. ST-AGNN model can extract the spatial interaction features of each time step and stack the spatial features to obtain the spatial-temporal features. It can predict the future trajectory by using Temporal Convolutional Network (TCN). We evaluate the average displacement error (ADE) and final displacement error (FDE) of the proposed method on ETH and UCY datasets. The experimental results show that the proposed method is effective in accuracy and efficiency.

Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and then, the feature extraction is performed by the self-attention mechanism to finally obtain the prediction results. The input and output lengths of this network can be determined flexibly. The validation of the method is carried out with three different multivariate time-series datasets. The reliability and accuracy of the prediction results are compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Temporal Convolutional Network (TCN). The prediction results show that the model proposed in this paper has significantly improved prediction accuracy and generalization.

On-line social networks (OSNs) often contain many different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily studied. There has also been prior work on link prediction in interaction networks,independent of friendship networks. In this paper, we study the predictive power of combining friendship and interaction networks. We hypothesize that, by leveraging friendship networks, we can improve the accuracy of link prediction in interaction networks. We augment several interaction link prediction algorithms to incorporate friendships and predicted friendships. From experiments on Facebook data, we find that incorporating friendships into interaction link prediction algorithms results in higher accuracy, but incorporating predicted friendships does not when compared to incorporating current friendships.

Introduction Network representation can give a holistic view of relationships for biomedical entities through network topology. Link prediction estimates the probability of link formation between the pair of unconnected nodes. In the drug discovery process, the link prediction method not only enables the detection of connectivity patterns but also predicts the effects of one biomedical entity to multiple entities simultaneously and vice versa, which is useful for many applications. Areas covered The authors provide a comprehensive overview of network link prediction in drug discovery. Link prediction methodologies such as similarity-based approaches, embedding-based approaches, probabilistic model-based approaches, and preprocessing methods are summarized with examples. In addition to describing their properties and limitations, the authors discuss the applications of link prediction in drug discovery based on the relationship between biomedical concepts. Expert opinion Link prediction is a powerful method to infer the existence of novel relationships in drug discovery. However, link prediction has been hampered by the sparsity of data and the lack of negative links in biomedical networks. With preprocessing to balance positive and negative samples and the collection of more data, the authors believe it is possible to develop more reliable link prediction methods that can become invaluable tools for successful drug discovery.