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847 results on '"Jiajun Bu"'

179. TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks

Author

Jiajun Bu, Zhao Li, Chengwei Yao, Zhen Zhang, Jia Wu, and Can Wang

Subjects
0209 industrial biotechnology, Theoretical computer science, Artificial neural network, Graph embedding, Computer science, Cognitive Neuroscience, Node (networking), Context (language use), 02 engineering and technology, Graph, Visualization, 020901 industrial engineering & automation, Artificial Intelligence, Computer Graphics, 0202 electrical engineering, electronic engineering, information engineering, Bipartite graph, Embedding, 020201 artificial intelligence & image processing, Neural Networks, Computer, Representation (mathematics)
Abstract

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node’s multifaceted properties. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.

Published
2021

180. Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads

Author

Jiajun Bu, Jiadong Zhang, Tao Gu, Yi Gao, Borui Li, Gaoyang Guan, and Wei Dong

Subjects
Computer Networks and Communications, business.industry, Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 02 engineering and technology, business, Internet of Things, Wireless sensor network, Limited resources, Edge computing, 020202 computer hardware & architecture, Computer network
Abstract

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.

Published
2021

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