TopPPR

Wei, Zhewei; He, Xiaodong; Xiao, Xiaokui; Wang, Sibo; Shang, Shuo; Wen, Ji-Rong

doi:10.1145/3183713.3196920

Cited by 61 publications

(5 citation statements)

References 39 publications

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“…The larger the node's degree is, the more likely the node is selected into 𝑄 2 . Note that the PageRank distribution of a real-world network is experimentally observed to follow the power-law distribution [4,26,37,38]. In particular, the power-law exponent of the PageRank distribution is the same as that of the degree distribution of the network.…”

Section: Methodsmentioning

confidence: 97%

Practices of education for sustainability in Renmin University of China

Liu¹

2017

Education and Sustainability

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Auto-evaluation aims to automatically evaluate a trained model on any test dataset without human annotations. Most existing methods utilize global statistics of features extracted by the model as the representation of a dataset. This ignores the influence of the classification head and loses category-wise confusion information of the model. However, ratios of instances assigned to different categories together with their confidence scores reflect how many instances in which categories are difficult for the model to classify, which contain significant indicators for both overall and category-wise performances. In this paper, we propose a Confidence-based Category Relation-aware Regression (C 2 R 2 ) method. C 2 R 2 divides all instances in a meta-set into different categories according to their confidence scores and extracts the global representation from them. For each category, C 2 R 2 encodes its local confusion relations to other categories into a local representation. The overall and category-wise performances are regressed from global and local representations, respectively. Extensive experiments show the effectiveness of our method.

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Section: Methodsmentioning

confidence: 97%

Practices of education for sustainability in Renmin University of China

Liu¹

2017

Education and Sustainability

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“…Although there exist algorithms [13,14,18,20,[38][39][40][41][42][43][44][45] that efficiently calculate the approximate PPR values, in this paper, we focus on this matrix form to calculate the proximity matrix due to its mathematically clean definition for analysis. Frequently used notations can be found in Appendix A.…”

Section: Preliminaries 21 Backgroundmentioning

confidence: 99%

Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective

Zhang,

Weng,

Wang

2024

Proceedings of the ACM on Web Conference 2024

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Node embedding learns low-dimensional vectors for nodes in the graph. Recent state-of-the-art embedding approaches take Personalized PageRank (PPR) as the proximity measure and factorize the PPR matrix or its adaptation to generate embeddings. However, little previous work analyzes what information is encoded by these approaches, and how the information correlates with their superb performance in downstream tasks. In this work, we first show that state-of-the-art embedding approaches that factorize a PPR-related matrix can be unified into a closed-form framework. Then, we study whether the embeddings generated by this strategy can be inverted to better recover the graph topology information than random-walk based embeddings. To achieve this, we propose two methods for recovering graph topology via PPR-based embeddings, including the analytical method and the optimization method. Extensive experimental results demonstrate that the embeddings generated by factorizing a PPR-related matrix maintain more topological information, such as common edges and community structures, than that generated by random walks, paving a new way to systematically comprehend why PPR-based node embedding approaches outperform random walk-based alternatives in various downstream tasks. To the best of our knowledge, this is the first work that focuses on the interpretability of PPR-based node embedding approaches.

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“…The Monte Carlo algorithm exploits short random walk segments starting at each node in the graph. Wei et al proposed TopPPR to compute the top- k entries up to a user specified precision [ 12 ]. TopPPR minimizes the accuracy loss through random walk sampling, forward search, and backward search in large graphs and also ensures accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Many researchers have made efforts to solve the overhead of graph processing based on various software approaches [ 11 , 12 , 13 , 14 , 15 ], but they still use a large memory size. Furthermore, though researchers have also proposed graph-processing accelerators [ 9 , 16 , 17 , 18 , 19 ], such a fixed hardware accelerator, they cannot provide flexibility in graph processing.…”

Section: Introductionmentioning

confidence: 99%

HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge

Gong

2023

Micromachines

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Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to guarantee data privacy and network latency. However, since PPR has a variety of computation/memory characteristics that vary depending on the graph datasets, it causes performance/energy inefficiency when it is executed on edge devices with limited hardware resources. In this paper, we propose HedgeRank, a heterogeneity-aware, energy-efficient, partitioning technique of personalized PageRank at the edge. HedgeRank partitions the PPR subprocesses and allocates them to appropriate edge devices by considering their computation capability and energy efficiency. When combining low-power and high-performance edge devices, HedgeRank improves the execution time and energy consumption of PPR execution by up to 26.7% and 15.2% compared to the state-of-the-art PPR technique.

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TopPPR

Cited by 61 publications

References 39 publications

Practices of education for sustainability in Renmin University of China

Practices of education for sustainability in Renmin University of China

Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective

HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge

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