High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration

The discovery of gene-disease associations is important for preventing, diagnosing, and treating diseases. In this paper, we propose two heterogeneous network-based methods that enhance gene-disease association prediction by using graph embedding and ensemble learning, abbreviated as 'HNEEM' and 'HNEEM-PLUS'. We integrate gene-disease associations, gene-chemical associations, gene-gene associations and disease-chemical associations to construct a heterogeneous network, and adopt six graph embedding methods respectively to learn the representative vectors of genes and diseases from the network. We build individual prediction models using each graph embedding representation and random forest, and then combine them by average scoring to construct the ensemble model HNEEM. To increase the diversity of base predictors, we further introduce the multilayer perceptron as an additional classifier and generate more base predictors, and thus propose an extended method named 'HNEEM-PLUS'. Computational experiments show that HNEEM has better results than individual methods and HNEEM-PLUS makes more improvement than HNEEM.

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.

In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

On connections between Rényi entropy Principal Component Analysis, kernel learning and graph embedding

Steiner tree problem (STP) in graphs aims to find a tree of minimum weight in the graph that connects a given set of vertices. It is a classic NP-hard combinatorial optimization problem and has many applications in real world. Many approximate algorithms have been developed for STP, but they suffer from high computational complexity and weak worst-case solution guarantees, respectively. Heuristic algorithms are also developed. But each of them requires application domain knowledge to design and is only suitable for specific scenarios. In this paper, we design a novel framework Cherrypick based on novel graph embedding and deep reinforcement learning to tackle the STP. Given an STP instance, Cherrypick uses this embedding to encode its path-related information and sends the encoded graph to a deep reinforcement learning component based on deep Q network (DQN) to find solutions. We implement the Cherrypick and demonstrate its efficacy and efficiency with extensive experiments using real-world and synthetic datasets.

Author response for "Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations"

Abstract. Intermodal transportation, using multiple modes in a single journey, promotes sustainable logistics. This paper addresses the Intermodal Vehicle Routing Problem and proposes GAGE-Q, which integrates graph embedding and Reinforcement Learning into a Genetic Algorithm. Our method models cities and their multi-modal connections as a graph, leveraging embedding for spatial dependencies and RL-based crossover for faster convergence and better solutions. Experiments on synthetic and real-world data show that GAGE-Q outperforms state-of-the-art methods, offering improved solution quality and efficiency in intermodal route planning.

PeerRemove: An adaptive node removal strategy for P2P botnet based on deep reinforcement learning

Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classification. However, existing embedding algorithms have two major limitations. First, they primarily focus on preserving one-to-one topological relationships between nodes (i.e., regions of interest (ROIs) in a connectome), but they have mostly ignored many-to-many relationships (i.e., set to set), which can be captured using a hyperconnectome structure. Second, existing graph embedding techniques cannot be easily adapted to multi-view graph data with heterogeneous distributions. In this paper, while cross-pollinating adversarial deep learning with hypergraph theory, we aim to jointly learn deep latent embeddings of subject-specific multi-view brain graphs to eventually disentangle different brain states such as Alzheimer’s disease (AD) versus mild cognitive impairment (MCI). First, we propose a new simple strategy to build a hyperconnectome for each brain view based on nearest neighbour algorithm to preserve the connectivities across pairs of ROIs. Second, we design a hyperconnectome autoencoder (HCAE) framework which operates directly on the multi-view hyperconnectomes based on hypergraph convolutional layers to better capture the many-to-many relationships between brain regions (i.e., graph nodes). For each subject, we further regularize the hypergraph autoencoding by adversarial regularization to align the distribution of the learned hyperconnectome embeddings with the original hyperconnectome distribution. We formalize our hyperconnectome embedding within a geometric deep learning framework to optimize for a given subject, thereby designing an individual-based learning framework. Our experiments showed that the learned embeddings by HCAE yield to better results for AD/MCI classification compared with deep graph-based autoencoding methods.

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed graph convolutional networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN-based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high-quality graph’s node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. We design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show the encouraging performance of HeteGraph on the first task and the state-of-the-art performance on the second task.

Graph embedding aims to learn the low-dimensional representation of nodes in the network, which has been paid more and more attention in many graph-based tasks recently. Graph Convolution Network (GCN) is a typical deep semisupervised graph embedding model, which can acquire node representation from the complex network. However, GCN usually needs to use a lot of labeled data and additional expressive features in the graph embedding learning process, so the model cannot be effectively applied to undirected graphs with only network structure information. In this paper, we propose a novel unsupervised graph embedding method via hierarchical graph convolution network (HGCN). Firstly, HGCN builds the initial node embedding and pseudo-labels for the undirected graphs, and then further uses GCNs to learn the node embedding and update labels, finally combines HGCN output representation with the initial embedding to get the graph embedding. Furthermore, we improve the model to match the different undirected networks according to the number of network node label types. Comprehensive experiments demonstrate that our proposed HGCN and HGCN∗ can significantly enhance the performance of the node classification task.

Recently, deep semi-supervised graph embedding learning has shown much promise on text and image recognition tasks when the number of labeled data is limited. By introducing an auxiliary unsupervised task of predicting the neighborhood context in the graph, these approaches effectively mine the structure information provided by abundant unlabeled data. However, existing methods usually adapt to datasets whose graph is explicitly given or predefined, which cannot handle large-scale datasets with unknown graphs. Besides, the edge connections and weights are fixed during the training process in these methods, which fails to use the current feature information extracted by the model. In this paper, we propose a novel deep semi-supervised dynamic anchor graph embedding learning algorithm. More specifically, we build a two-branch architecture to learn the single-sample local features and the global features in the graph simultaneously. The first branch constrains its outputs to be consistent with different perturbations of the same single sample. And the output features are dedicated to constructing the dynamic anchor graph. The second branch utilizes the graph and the model prediction to sample the context. Then the global graph embed dings are learned based on the context. Finally, the outputs of the two branches are aggregated to jointly predict the class labels. Extensive experimental results on several image and text datasets have shown that the proposed method is able to improve the performance of existing graph embedding learning methods and outperform many state-of-the-art methods on image classification.

Knowledge Graph Embedding (KGE) is to learn continuous vectors of entities and relations in the Knowledge Graph (KG). Inspired by the R-GCN model, we propose a novel embedding learning model named RotatGAT, which combines the RotatE model and the GAT model. The goal is to overcome the shortcomings of R-GCN, that has a relatively high computing complexity and cannot distinguish the importance of neighbors. We introduce the RotatE model into RotatGAT to represent the embeddings of heterogeneous entities and relations in KG. Considering RotatE cannot use the structure information to learn entities' embeddings, we introduce the GAT model to learn the importance of neighbors of an entity and aggregate the feature information of neighbors for graph embedding learning. The link prediction experiments show the overall performance of RotatGAT on four benchmark datasets outperforms existing state-of-the-art models.

Graph embedding in vector spaces by node attribute statistics

Search and Rescue or surveillance applications from en embedded moving camera yield challenging computer vision problems as both very high precision/recall and real-time performance are required. However, in these contexts, it is often sufficient to assess the detection of each object of interest only once in the area spanned by the camera, with the idea that what matters is to be aware of its existence, its time to detection being a secondary objective. Taking advantage of this point, we describe a sparse box proposal controlled by tracking and designed to generate a small number of boxes per frame covering each object at least once in the video. Our sparse box proposal adapts to the budget allowed for single box classification and is able to achieve relevant results even on challenging situations while comfortably dealing with real time requirements.