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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Zhang, Minjia Wang, Wenhan He, Yuxiong Microsoft Bellevue WA 98004 USA

ISBN: (纸本)9781450391320

Nearest Neighbor Search (NNS) has recently drawn a rapid growth of interest because of its core role in high-dimensional vector data management in data science and AI applications. The interest is fueled by the success of neural embedding, where deep learning models transform unstructured data into semantically correlated feature vectors for data analysis, e.g., recommending popular items. Among several categories of methods for fast NNS, graph-based approximate nearest neighbor search algorithms have led to the best-in-class search performance on a wide range of real-world datasets. While prior works improve graph-based NNS search efficiency mainly through exploiting the structure of the graph with sophisticated heuristic rules, in this work, we show that the frequency distributions of edge visits for graph-based NNS can be highly skewed. This finding leads to the study of pruning unnecessary edges to avoid redundant computation during graph traversal by utilizing the query distribution, an important yet under-explored aspect of graph-based NNS. In particular, we formulate graph pruning as a discrete optimization problem, and introduce a graph optimization algorithm GraSP that improves the search efficiency of similarity graphs by learning to prune redundant edges. GraSP enhances an existing similarity graph with a probabilistic model. It then performs a novel subgraph sampling and iterative refinement optimization to explicitly maximize search efficiency when removing a subset of edges in expectation over a graph for a large set of training queries. The evaluation shows that GraSP consistently improves the search efficiency on real-world datasets, providing up to 2.24X faster search speed than state-of-the-art methods without losing accuracy.

关键词： Vector management and search search efficiency graph sampling

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Fast MSE-Based sampling of Bandlimited graph Signals via Low-Pass Impulse Responses

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 4207-4223页

作者： Wang, Fen Cheung, Gene Ye, Minxiang Li, Taihao Feng, Yi-Tian Zhejiang Lab Hangzhou 311121 Peoples R China York Univ Dept EECS Toronto ON M3J 1P3 Canada

sampling is a fundamental problem in graph signal processing that selects a node subset to collect samples, so that data in the remaining nodes can be well recovered. Existing eigen-decomposition-free (ED-free) graph sampling schemes are not designed to minimize mean square error (MMSE) of reconstructed bandlimited graph signals, often resulting in sub-par MSE performance. In this paper, we propose a lightweight ED-free algorithm to minimize an approximate MSE objective using per-node impulse responses of an ideal low-pass graph filter. Specifically, we first derive a proxy of the MMSE greedy sampling objective without matrix inverse. We then define node-dependent impulse responses of an ideal low-pass graph filter-analogous to the sinc function in traditional signal processing-which can be approximated very fast without ED. We use these response vectors to reformulate our derived sampling objective, then show that the optimal sampling set has supported low-pass impulse responses that are as orthogonal as possible-defaulting to uniform sampling for 1D regular kernels when the graph Fourier basis is DFT. For optimization, we propose a fast sampling algorithm to evaluate candidates via vector-vector multiplications by reusing previous greedy results. For faster sampling, we relax the MMSE-based greedy objective to a bounded approximation, so that candidate nodes can be easily appraised using simple scalar multiplications. Extensive experiments show that our sampling method achieved state-of-the-art sampling speed and had the best MSE performance among deterministic ED-free sampling methods in various scenarios.

关键词： Laplace equations Complexity theory Signal processing algorithms sampling methods Matrix decomposition Eigenvalues and eigenfunctions Approximation algorithms graph sampling graph signal processing optimality design eigen-decomposition

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Context-aware sampling of Large Networks via graph Representation Learning

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER graphICS 2021年第2期27卷 1709-1719页

作者： Zhou, Zhiguang Shi, Chen Shen, Xilong Cai, Lihong Wang, Haoxuan Liu, Yuhua Zhao, Ying Chen, Wei Zhejiang Univ Finance & Econ Sch Informat Hangzhou Peoples R China Cent South Univ Changsha Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China

Numerous sampling strategies have been proposed to simplify large-scale networks for highly readable visualizations. It is of great challenge to preserve contextual structures formed by nodes and edges with tight relationships in a sampled graph, because they are easily overlooked during the process of sampling due to their irregular distribution and immunity to scale. In this paper, a new graph sampling method is proposed oriented to the preservation of contextual structures. We first utilize a graph representation learning (GRL) model to transform nodes into vectors so that the contextual structures in a network can be effectively extracted and organized. Then, we propose a multi-objective blue noise sampling model to select a subset of nodes in the vectorized space to preserve contextual structures with the retention of relative data and cluster densities in addition to those features of significance, such as bridging nodes and graph connections. We also design a set of visual interfaces enabling users to interactively conduct context-aware sampling, visually compare results with various sampling strategies, and deeply explore large networks. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of our method in the abstraction and exploration of large networks.

关键词： Visualization Measurement sampling methods Context modeling Task analysis Clutter Layout graph sampling graph representation learning Blue noise sampling graph evaluation

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graph Signal Reconstruction Under Heterogeneous Noise via Adaptive Uncertainty-Aware sampling and Soft Classification

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 277-293页

作者： Fascista, Alessio Coluccia, Angelo Ravazzi, Chiara Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy Italian Natl Res Council CNR IEIIT I-10129 Corso Duca Degli Abruzzi Italy

Reconstructing bandlimited graph signals from a subset of noisy measurements is a fundamental challenge within the realm of signal processing. Historically, this problem has been approached assuming uniform noise variance across the network. Nevertheless, practical scenarios often present heterogeneous noise landscapes, greatly complicating the signal reconstruction process. This study tackles reconstruction of graph signals across networks where measurements may be affected by heterogeneous noise. A Bayesian model tailored for graph signals is employed, considering the potential existence of node-specific variations in measurement variance, namely different (and unknown) levels of uncertainty. Moreover, a novel uncertainty-aware local graph coherence metric is introduced, capitalizing on estimated parameters to refine the sampling process. By accommodating uncertainty, signal reconstruction accuracy is enhanced, even in demanding noise conditions. The proposed approach revolves around a framework combining maximum likelihood and maximum a-posteriori principles. Specifically, each observation is weighted based on a soft classification of nodes, so incorporating measurements reliability into the reconstruction process. The latter is performed through a novel algorithm coupling re-weighted iterative least squares with expectation-maximization. Such an algorithm can effectively manage heterogeneous noise and features a non-local regularization term, which promotes sparsity in the reconstructed signal while preserving signal discontinuities, crucial for capturing the characteristics of the underlying graph signal. Extensive simulations demonstrate the effectiveness of the proposed approach for various graph topologies and anomalous conditions, revealing substantial enhancements in signal reconstruction compared to existing methods. An illustrative example on PM10 data from the European Copernicus Atmosphere Monitoring Service (CAMS) is also reported.

关键词： Anomaly detection graph sampling graph signal reconstruction heterogeneous noise joint ML-MAP

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Point Cloud sampling via graph Balancing and Gershgorin Disc Alignment

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第1期45卷 868-886页

作者： Dinesh, Chinthaka Cheung, Gene Bajic, Ivan V. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

Point cloud (PC)& mdash;a collection of discrete geometric samples of a 3D object's surface & mdash;is typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of practical importance. Previous model-based sub-sampling schemes are ad-hoc in design and do not preserve the overall shape sufficiently well, while previous data-driven schemes are trained for specific pre-determined input PC sizes and sub-sampling rates and thus do not generalize well. Leveraging advances in graph sampling, we propose a fast PC sub-sampling algorithm of linear time complexity that chooses a 3D point subset while minimizing a global reconstruction error. Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-res PC, given points chosen by a sampling matrix H. We prove that minimizing a worst-case SR reconstruction error is equivalent to maximizing the smallest eigenvalue Aminof matrix HTH + mG, where G is a symmetric, positive semi-definite matrix derived from a neighborhood graph connecting the 3D points. To arrive at a fast algorithm, instead of maximizing Amin, we maximize a lower bound A-min(HTH + mG) via selection of H & mdash;this translates to a graph sampling problem for a signed graph g with self-loops specified by graph Laplacian G. We tackle this general graph sampling problem in three steps. First, we approximate g with a balanced graph gB specified by Laplacian GB. Second, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we perform a similarity transform Gp = SGBS-1, so that all Gershgorin disc left-ends of Gp are aligned exactly at Amin(GB). Finally, we choose samples on gB using a previous graph sampling algorithm to maximize A-min(HTH +mGp) in linear time. Experimental results show that 3D points chosen by our algorithm outperformed competing schemes both numerically

关键词： Point cloud processing graph signal processing graph sampling Gershgorin circle theorem graph Laplacian regularizer

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Fedgraph: Federated graph Learning With Intelligent sampling

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第8期33卷 1775-1786页

作者： Chen, Fahao Li, Peng Miyazaki, Toshiaki Wu, Celimuge Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Univ Aizu Aizu Wakamatsu Fukushima 9658580 Japan Univ Electrocommun Chofu Tokyo 1828585 Japan

Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. However, existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), which cannot efficiently handle graph data that are popular in many applications. graph Convolutional Network (GCN) has been proposed as one of the most promising techniques for graph learning, but its federated setting has been seldom explored. In this article, we propose Fedgraph for federated graph learning among multiple computing clients, each of which holds a subgraph. Fedgraph provides strong graph learning capability across clients by addressing two unique challenges. First, traditional GCN training needs feature data sharing among clients, leading to risk of privacy leakage. Fedgraph solves this issue using a novel cross-client convolution operation. The second challenge is high GCN training overhead incurred by large graph size. We propose an intelligent graph sampling algorithm based on deep reinforcement learning, which can automatically converge to the optimal sampling policies that balance training speed and accuracy. We implement Fedgraph based on PyTorch and deploy it on a testbed for performance evaluation. The experimental results of four popular datasets demonstrate that Fedgraph significantly outperforms existing work by enabling faster convergence to higher accuracy.

关键词： Training Convolution Collaborative work Servers Privacy Computational modeling Convolutional neural networks Federated learning graph learning graph sampling reinforcement learning

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A novel fuzzy knowledge graph structure for decision making of multimodal big data

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APPLIED INTELLIGENCE 2025年第6期55卷 1-21页

作者： Tan, Nguyen Hong Long, Cu Kim Tuan, Tran Manh Chuan, Pham Minh Hai, Pham Van Khanh, Phan Hung Son, Le Hoang Grad Univ Sci & Technol Acad Sci & Technol Hanoi Vietnam Informat Commun & Technol Univ ICTU Fac Informat Technol Thai Nguyen Vietnam Minist Sci & Technol MOST Informat Technol Ctr Hanoi Vietnam Thuyloi Univ Fac Comp Sci & Engn Hanoi Vietnam Hung Yen Univ Technol & Educ Fac Informat Technol Hung Yen Vietnam Hanoi Univ Sci & Technol HUST Sch Informat & Commun Technol Hanoi Vietnam Vietnam Natl Univ VNU Informat Technol Inst Artificial Intelligence Res Ctr Hanoi Vietnam

Decision-making in the era of big data is always a challenge. Recently, various methods especially graph sampling have been presented to assist the decision more effectively. As real-world graphs are large, constantly evolving, and distributed in nature, it becomes necessary to sample their structures for many different goals. Therefore, acquiring a comprehensive and in-depth understanding of graph sampling is essential to strengthen this field. In addition, graph sampling techniques often rely on edge or vertex sampling without effective methods for rule or path sampling. In this paper, we propose a novel framework for the rule-based sampling method on fuzzy knowledge graphs. In this framework, fuzzy knowledge graphs are built on integrated databases from multiple sources. We design a purposive random sampling method based on fuzzy rules on graphs to prioritize important rules for output inference. The remaining important rules form the core structure of the fuzzy knowledge graph, known as the Fuzzy Knowledge graph Structure (FKGS). This structure is considered as a compression mechanism to reduce computational complexity when representing and performing calculations for large-scale data problems. Experimental results based on benchmark datasets on diabetes mellitus show that the sampling method greatly reduces the calculation time while maintaining high accuracy. Moreover, the purposive random sampling method results in significantly higher accuracy than the random sampling method. Besides, the ANOVA method is also conducted to statistically validate the model. The results are significant for decision-making in the context of big data.

关键词： Fuzzy knowledge graph graph sampling Decision making Big data Fuzzy knowledge graph structure

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Triangle-induced and degree-wise sampling over large graphs in social networks

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-29页

作者： Gavagsaz, Elaheh Souri, Alireza Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Halic Univ Fac Engn Dept Comp Engn TR-34060 Istanbul Turkiye

Social networks are crucial channels for information dissemination because they facilitate the effective exchange of ideas and information. The extensive utilization of these networks in daily life results in their explosive growth. Most methods are not practical for large-scale network analysis. Hence, analyzing a small portion instead of the whole network could be preferable, a technique known as graph sampling. According to a literature review, many approaches to graph sampling focus only on representing connectivity from triangle count assessment or node degree evaluation rather than both. This paper introduces an innovative method called TDGS (triangle-induced and degree-wise graph sampling) for producing a sample under the impact of both triangle count and node degree in social networks. The key idea behind TDGS is that it uses a centrality measure based on the degree centrality and local information of nodes about the connectivity of their neighbors to guide the sampling process. Furthermore, TDGS proposes a distributed model that can handle large-scale graphs. We evaluate the performance of our proposed method using real-world social networks. The experimental results show that TDGS provides significantly more precise information about node degrees than the well-known graph sampling methods and can estimate the global clustering coefficient with fewer estimation errors.

关键词： Social network graph sampling Clustering coefficient Node degrees Distributed model

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Random walks, equidistribution and graphical designs

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ADVANCES IN APPLIED MATHEMATICS 2025年 165卷

作者： Steinerberger, Stefan Thomas, Rekha R. Univ Washington Dept Math Seattle WA 98195 USA

Let G = (V, E) be a d-regular graph on n vertices and let mu 0 be a probability measure on V. The act of moving to a randomly chosen neighbor leads to a sequence of probability measures supported on V given by mu(k +1) = AD(-1)mu(k), where A is the adjacency matrix and D is the diagonal matrix of vertex degrees of G. Ordering the eigenvalues of AD(-1) as 1 = lambda(1) >=|lambda(2)|>= center dot center dot center dot >= |lambda(n)|>= 0, it is well-known that the graphs for which |lambda 2| is small are those in which the random walk process converges quickly to the uniform distribution: for all initial probability measures mu(0) and all k >= 0, Sigma (v is an element of V) | mu(k)(v) - 1/n | (2) <= c lambda(2k)(2) . One could wonder whether this rate can be improved for specific initial probability measures mu 0. We show that if G is regular, then for any 1 <= l <= n, there exists a probability measure mu 0 supported on at most l vertices so that Sigma (v is an element of V) | mu(k)(v) - 1/n | (2) <= c lambda (2k)(l+1) .

关键词： graphical design Random walk graph sampling graph clustering

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Unbiased sampling in Directed Social graph 10

Unbiased Sampling in Directed Social Graph

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ACM SIGCOMM Conference 2010

作者： Wang, Tianyi Chen, Yang Zhang, Zengbin Sun, Peng Deng, Beixing Li, Xing Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Goettingen Inst Comp Sci Gottingen Germany Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

ISBN: (纸本)9781450302012

Microblogging services, such as Twitter, are among the most important online social networks(OSNs). Different from OSNs such as Facebook, the topology of microblogging service is a directed graph instead of an undirected graph. Recently, due to the explosive increase of population size, graph sampling has started to play a critical role in measurement and characterization studies of such OSNs. However, previous studies have only focused on the unbiased sampling of undirected social graphs. In this paper, we study the unbiased sampling algorithm for directed social graphs. Based on the traditional Metropolis-Hasting Random Walk (MHRW) algorithm, we propose an unbiased sampling method for directed social graphs(USDSG). Using this method, we get the first, to the best of our knowledge, unbiased sample of directed social graphs. Through extensive experiments comparing with the "ground truth" (UNI, obtained through uniform sampling of directed graph nodes), we show that our method can achieve excellent performance in directed graph sampling and the error to UNI is less than 10%.

关键词： Human Factors Measurement Online social network graph sampling unbias

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