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1. BRIGHT: A Bridging Algorithm for Network Alignment

   Yuchen Yan, Si Zhang (April 2021, TheWebConf)

   null (Ed.)

   Multiple networks emerge in a wealth of high-impact applications. Network alignment, which aims to find the node correspondence across different networks, plays a fundamental role for many data mining tasks. Most of the existing methods can be divided into two categories: (1) consistency optimization based methods, which often explicitly assume the alignment to be consistent in terms of neighborhood topology and attribute across networks, and (2) network embedding based methods which learn low-dimensional node embedding vectors to infer alignment. In this paper, by analyzing certain methods of these two categories, we show that (1) the consistency optimization based methods are essentially specific random walk propagations from anchor links that might be restrictive; (2) the embedding based methods no longer explicitly assume alignment consistency but inevitably suffer from the space disparity issue. To overcome these two limitations, we bridge these methods and propose a novel family of network alignment algorithms BRIGHT to handle both non-attributed and attributed networks. Specifically, it constructs a space by random walk with restart (RWR) whose bases are one-hot encoding vectors of anchor nodes, followed by a shared linear layer. Our experiments on real-world networks show that the proposed family of algorithms BRIGHT outperform the state-of-the- arts for both non-attributed and attributed network alignment tasks. 

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2. ATTENT: Active Attributed Network Alignment

   Qinghai Zhou, Liangyue Li (April 2021, TheWebConf)

   null (Ed.)

   Network alignment finds node correspondences across multiple networks, where the alignment accuracy is of crucial importance because of its profound impact on downstream applications. The vast majority of existing works focus on how to best utilize the topology and attribute information of the input networks as well as the anchor links when available. Nonetheless, it has not been well studied on how to boost the alignment performance through actively obtaining high-quality and informative anchor links, with a few exceptions. The sparse literature on active network alignment introduces the human in the loop to label some seed node correspondence (i.e., anchor links), which are informative from the perspective of querying the most uncertain node given few potential matchings. However, the direct influence of the intrinsic network attribute information on the alignment results has largely remained unknown. In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. The key idea of the proposed method is to leverage effective and efficient influence functions defined over the alignment solution to evaluate the goodness of the candidate nodes for query. Our proposed query strategy bears three distinct advantages, including (1) effectiveness, being able to accurately quantify the influence of the candidate nodes on the alignment results; (2) efficiency, scaling linearly with 15 − 17× speed-up over the straightforward implementation without any quality loss; (3) generality, consistently improving alignment performance of a variety of network alignment algorithms. 

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3. FedDNA: Federated learning using dynamic node alignment

   https://doi.org/10.1371/journal.pone.0288157  

   Wang, Shuwen; Zhu, Xingquan (July 2023, PLOS ONE)

   Donta, Praveen Kumar (Ed.)

   Federated Learning (FL), as a new computing framework, has received significant attentions recently due to its advantageous in preserving data privacy in training models with superb performance. During FL learning, distributed sites first learn respective parameters. A central site will consolidate learned parameters, using average or other approaches, and disseminate new weights across all sites to carryout next round of learning. The distributed parameter learning and consolidation repeat in an iterative fashion until the algorithm converges or terminates. Many FL methods exist to aggregate weights from distributed sites, but most approaches use a static node alignment approach, where nodes of distributed networks are statically assigned, in advance, to match nodes and aggregate their weights. In reality, neural networks, especially dense networks, have nontransparent roles with respect to individual nodes. Combined with random nature of the networks, static node matching often does not result in best matching between nodes across sites. In this paper, we propose, FedDNA, adynamic node alignmentfederated learning algorithm. Our theme is to find best matching nodes between different sites, and then aggregate weights of matching nodes for federated learning. For each node in a neural network, we represent its weight values as a vector, and use a distance function to find most similar nodes,i.e., nodes with the smallest distance from other sides. Because finding best matching across all sites are computationally expensive, we further design a minimum spanning tree based approach to ensure that a node from each site will have matched peers from other sites, such that the total pairwise distances across all sites are minimized. Experiments and comparisons demonstrate that FedDNA outperforms commonly used baseline, such as FedAvg, for federated learning. 

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4. ORIGIN: Non-Rigid Network Alignment

   https://doi.org/10.1109/BigData47090.2019.9005663  

   Zhang, Si; Tong, Hanghang; Xu, Jiejun; Hu, Yifan; Maciejewski, Ross (December 2019, BigData)

   Network alignment is a fundamental task in many high-impact applications. Most of the existing approaches either explicitly or implicitly consider the alignment matrix as a linear transformation to map one network to another, and might overlook the complicated alignment relationship across networks. On the other hand, node representation learning based alignment methods are hampered by the incomparability among the node representations of different networks. In this paper, we propose a unified semi-supervised deep model (ORIGIN) that simultaneously finds the non-rigid network alignment and learns node representations in multiple networks in a mutually beneficial way. The key idea is to learn node representations by the effective graph convolutional networks, which subsequently enable us to formulate network alignment as a point set alignment problem. The proposed method offers two distinctive advantages. First (node representations), unlike the existing graph convolutional networks that aggregate the node information within a single network, we can effectively aggregate the auxiliary information from multiple sources, achieving far-reaching node representations. Second (network alignment), guided by the highquality node representations, our proposed non-rigid point set alignment approach overcomes the bottleneck of the linear transformation assumption. We conduct extensive experiments that demonstrate the proposed non-rigid alignment method is (1) effective, outperforming both the state-of-the-art linear transformation-based methods and node representation based methods, and (2) efficient, with a comparable computational time between the proposed multi-network representation learning component and its single-network counterpart. 

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5. Anchor Selection for Topology Inference and Routing in Wireless Sensor Networks

   https://doi.org/10.23919/JCIN.2020.9200895  

   Bouchoucha, Taha; Ding, Zhi (September 2020, Journal of communications and information networks)

   null (Ed.)

   Anchor-based ad-hoc networks utilize hop measurements to generate a virtual coordinate system for topology inference and routing applications. A common problem with such coordinate system is its sensitivity to anchor placement. We present a general formulation to the anchor node selection problem. Then, we relax the optimization problem by deriving an upper-bound of the objective function. We finally propose an iterative algorithm that consists in choosing additional anchor nodes based on the connectivity information provided by the current anchor set. Numerical simulations indicate that our anchor selection method is robust to missing measurements and improves network topology inference and routing performance. 

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