专栏首页专知【论文推荐】最新5篇网络节点表示(Network Embedding)相关论文—高阶网络、矩阵分解、多视角、虚拟网络、云计算

【论文推荐】最新5篇网络节点表示(Network Embedding)相关论文—高阶网络、矩阵分解、多视角、虚拟网络、云计算

【导读】专知内容组整理了最近五篇网络节点表示(Network Embedding)相关文章,为大家进行介绍,欢迎查看!

1. HONE: Higher-Order Network Embeddings(HONE:高阶网络嵌入)



作者:Ryan A. Rossi,Nesreen K. Ahmed,Eunyee Koh

摘要:This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations. In all cases, HONE outperforms recent embedding methods that are unable to capture higher-order structures with a mean relative gain in AUC of $19\%$ (and up to $75\%$ gain) across a wide variety of networks and embedding methods.

期刊:arXiv, 2018年1月29日

网址

http://www.zhuanzhi.ai/document/cc5ef37da6d757722a5f9c4fcc66bc23

2. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec(基于网络嵌入的矩阵分解统一形式:DeepWalk、LINE、PTE和node2vec)



作者:Jiezhong Qiu,Yuxiao Dong,Hao Ma,Jian Li,Kuansan Wang,Jie Tang

摘要:Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks' Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.

期刊:arXiv, 2017年12月12日

网址

http://www.zhuanzhi.ai/document/28e9c10a447f88c9fd23e7eee077e953

3. mvn2vec: Preservation and Collaboration in Multi-View Network Embedding(mvn2vec:基于多视角网络嵌入的保存和协同)



作者:Yu Shi,Fangqiu Han,Xinran He,Carl Yang,Jie Luo,Jiawei Han

摘要:Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machine-actionable. Meanwhile, network embedding has emerged as an effective approach to generate distributed network representations. Therefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are specific and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving better embedding quality by simultaneously modeling preservation and collaboration, and propose the mvn2vec algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further confirm the presence and importance of preservation and collaboration. These experiments also demonstrate that better embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.

期刊:arXiv, 2018年1月20日

网址

http://www.zhuanzhi.ai/document/9030e20eda802b73a0e067ebb6890bb8

4. MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops(MARVELO:无线虚拟网络嵌入的循环覆盖图)



作者:Haitham Afifi,Sebastien Auroux,Holger Karl

摘要:When deploying resource-intensive signal processing applications in wireless sensor or mesh networks, distributing processing blocks over multiple nodes becomes promising. Such distributed applications need to solve the placement problem (which block to run on which node), the routing problem (which link between blocks to map on which path between nodes), and the scheduling problem (which transmission is active when). We investigate a variant where the application graph may contain feedback loops and we exploit wireless networks? inherent multicast advantage. Thus, we propose Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays (MARVELO) to find efficient solutions for scheduling and routing under a detailed interference model. We cast this as a mixed integer quadratically constrained optimisation problem and provide an efficient heuristic. Simulations show that our approach handles complex scenarios quickly.

期刊:arXiv, 2017年12月19日

网址

http://www.zhuanzhi.ai/document/fdd40fc170d7973c720952fc82d0a935

5. Towards Efficient Dynamic Virtual Network Embedding Strategy for Cloud IoT Networks(面向云计算网络的高效动态虚拟网络嵌入策略)



作者:Duc-Lam Nguyen,HyungHo Byun,Naeon Kim,Chong-Kwon Kim

摘要:Network Virtualization is one of the most promising technologies for future networking and considered as a critical IT resource that connects distributed, virtualized Cloud Computing services and different components such as storage, servers and application. Network Virtualization allows multiple virtual networks to coexist on same shared physical infrastructure simultaneously. One of the crucial keys in Network Virtualization is Virtual Network Embedding, which provides a method to allocate physical substrate resources to virtual network requests. In this paper, we investigate Virtual Network Embedding strategies and related issues for resource allocation of an Internet Provider(InP) to efficiently embed virtual networks that are requested by Virtual Network Operators(VNOs) who share the same infrastructure provided by the InP. In order to achieve that goal, we design a heuristic Virtual Network Embedding algorithm that simultaneously embeds virtual nodes and virtual links of each virtual network request onto physic infrastructure. Through extensive simulations, we demonstrate that our proposed scheme improves significantly the performance of Virtual Network Embedding by enhancing the long-term average revenue as well as acceptance ratio and resource utilization of virtual network requests compared to prior algorithms.

期刊:arXiv, 2018年1月30日

网址

http://www.zhuanzhi.ai/document/d283dbfc0205aaa398addb5d20dd5de2

本文分享自微信公众号 - 专知(Quan_Zhuanzhi),作者:专知内容组(编)

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2018-02-10

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