Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that pass through them. It is key to many valuable applications, including community detection and network dismantling. Computing BC scores on large networks is computationally challenging due to high time complexity. Many approximation algorithms have been proposed to speed up the estimation of BC, which are mainly sampling-based. However, these methods are still prone to considerable execution time on large-scale networks, and their results are often exacerbated when small changes happen to the network structures. In this paper, we focus on identifying nodes with high BC in a graph, since many application scenarios are built upon retrieving nodes with top-k BC. Different from previous heuristic methods, we turn this task into a learning problem and design an encoder-decoder based framework to resolve the problem. More specifcally, the encoder leverages the network structure to encode each node into an embedding vector, which captures the important structural information of the node. The decoder transforms the embedding vector for each node into a scalar, which captures the relative rank of this node in terms of BC. We use the pairwise ranking loss to train the model to identify the orders of nodes regarding their BC. By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes. Comprehensive experiments on both synthetic and real-world networks demonstrate that, compared to representative baselines, our model drastically speeds up the prediction without noticeable sacrifce in accuracy, and outperforms the state-of-the-art by accuracy on several large real-world networks.
相關內容
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph inference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.
論文題目： Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
論文摘要：
Betweenness centrality (BC)是網絡分析中廣泛使用的一種中心性度量，它試圖通過最短路徑的比例來描述網絡中節點的重要性。它是許多有價值的應用的關鍵，包括社區檢測和網絡拆除。由于時間復雜度高，在大型網絡上計算BC分數在計算上具有挑戰性。許多基于采樣的近似算法被提出以加速BC的估計。然而，這些方法在大規模網絡上仍然需要相當長的運行時間，并且它們的結果對網絡的微小擾動都很敏感。
在這篇論文中，我們主要研究如何有效識別圖中BC最高的top k節點，這是許多網絡應用程序所必須完成的任務。與以往的啟發式方法不同，我們將該問題轉化為一個學習問題，并設計了一個基于encoder-decoder的框架作為解決方案。具體來說，encoder利用網絡結構將每個節點表示為一個嵌入向量，該嵌入向量捕獲節點的重要結構信息。decoder將每個嵌入向量轉換成一個標量，該標量根據節點的BC來標識節點的相對rank。我們使用pairwise ranking損失來訓練模型，以識別節點的BC順序。通過對小規模網絡的訓練，該模型能夠為較大網絡的節點分配相對BC分數，從而識別出高排名的節點。在合成網絡和真實世界網絡上的實驗表明，與現有的baseline相比，我們的模型在沒有顯著犧牲準確性的情況下大大加快了預測速度，甚至在幾個大型真實世界網絡的準確性方面超過了最先進的水平。
論文作者：
Muhao Chen在加州大學洛杉磯分校獲得了計算機科學博士學位，目前是丹·羅斯教授的博士后研究員。廣泛研究了機器學習和自然語言處理的主題，包括關系學習、序列建模、詞匯語義和圖表示學習。最近的研究全面擴展了表示學習模型，以捕獲多關系數據的各種屬性，包括可轉移性、不確定性和邏輯屬性。
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Meta-learning extracts the common knowledge acquired from learning different tasks and uses it for unseen tasks. It demonstrates a clear advantage on tasks that have insufficient training data, e.g., few-shot learning. In most meta-learning methods, tasks are implicitly related via the shared model or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can significantly improve the performance of few-shot learning. This type of graph is usually free or cheap to obtain but has rarely been explored in previous works. We study the prototype based few-shot classification, in which a prototype is generated for each class, such that the nearest neighbor search between the prototypes produces an accurate classification. We introduce "Gated Propagation Network (GPN)", which learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class benefits from the data of other related classes. In GPN, an attention mechanism is used for the aggregation of messages from neighboring classes, and a gate is deployed to choose between the aggregated messages and the message from the class itself. GPN is trained on a sequence of tasks from many-shot to few-shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, we change the training-test discrepancy and test task generation settings for thorough evaluations. GPN outperforms recent meta-learning methods on two benchmark datasets in all studied cases.
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