Tree++: Truncated Tree Based Graph Kernels

Ye, Wei; Wang, Zhen; Redberg, Rachel; Singh, Ambuj K.

doi:10.1109/tkde.2019.2946149

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…In the experiments, uniform weight λ is chosen from {10 −2 , 10 −1 , • • • , 10 2 }. We compare our kernel with state-of-the-art graph kernels including shortest path kernel (SP) [9], Weisfeiler-Lehman subtree kernel (WL-ST) [17], Weisfeiler-Lehman shortest path kernel (WL-SP) [17], random walk kernel (RW) [10], pyramid match kernel (PM) [21], Wasserstein Weisfeiler-Lehman graph kernel (WWL) [18], GraphHopper kernel (GH) [23], Truncated Tree Based Graph Kernels (Tree++) [24]. In robustness experiment, we randomly discard partial data in each brain network by 25% missing rate.…”

Section: Methodsmentioning

confidence: 99%

Ordinal Pattern Kernel for Brain Connectivity Network Classification

Jie

Shao

et al. 2020

Preprint

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Brain connectivity networks, which characterize the functional or structural interaction of brain regions, has been widely used for brain disease classification. Kernel-based method, such as graph kernel (i.e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance. However, most of graph kernels are built on unweighted graph (i.e., network) with edge present or not, and neglecting the valuable weight information of edges in brain connectivity network, with edge weights conveying the strengths of temporal correlation or fiber connection between brain regions. Accordingly, in this paper, we present an ordinal pattern kernel for brain connectivity network classification. Different with existing graph kernels that measures the topological similarity of unweighted graphs, the proposed ordinal pattern kernels calculate the similarity of weighted networks by comparing ordinal patterns from weighted networks. To evaluate the effectiveness of the proposed ordinal kernel, we further develop a depth-first-based ordinal pattern kernel, and perform extensive experiments in a real dataset of brain disease from ADNI database. The results demonstrate that our proposed ordinal pattern kernel can achieve better classification performance compared with state-of-the-art graph kernels.

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Section: Methodsmentioning

confidence: 99%

Ordinal Pattern Kernel for Brain Connectivity Network Classification

Jie

Shao

et al. 2020

Preprint

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“…In [51], the authors combine the standard shortest-path graph kernel [5] with a deep convolutional neural network to analyze network attacks. However, since shortest-paths do not consider neighborhood structures, the graph similarity is only captured at fine granularities [52]. Moreover, the shortest-path kernel requires a quartic time complexity in the size of the graphs and thus, is expensive to compute for very large graphs.…”

Section: Related Workmentioning

confidence: 99%

Nadege: When Graph Kernels meet Network Anomaly Detection

Lesfari

Giroire

2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

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With the continuous growing level of dynamicity, heterogeneity, and complexity of traffic data, anomaly detection remains one of the most critical tasks to ensure an efficient and flexible management of a network. Recently, driven by their empirical success in many domains, especially bioinformatics and computer vision, graph kernels have attracted increasing attention. Our work aims at investigating their discrimination power for detecting vulnerabilities and distilling traffic in the field of networking.In this paper, we propose Nadege, a new graph-based learning framework which aims at preventing anomalies from disrupting the network while providing assistance for traffic monitoring. Specifically, we design a graph kernel tailored for network profiling by leveraging propagation schemes which regularly adapt to contextual patterns. Moreover, we provide provably efficient algorithms and consider both offline and online detection policies. Finally, we demonstrate the potential of kernel-based models by conducting extensive experiments on a wide variety of network environments. Under different usage scenarios, Nadege significantly outperforms all baseline approaches.This work has been supported by the French government through the UCA JEDI (ANR-15-IDEX-01) and EUR DS4H (ANR-17-EURE-004) Investments in the Future projects and by Inria associated team EfdyNet.

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“…It counts the number of occurrences of each subtree pattern in graphs. 6) Tree++ [6] counts the number of occurrences of pathpatterns in graphs. It belongs to the R-convolution kernels without considering the distributions of the individual substructures in graphs.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…SP counts the number of pairs of matching shortest paths that have the same source and sink labels and the same length in two graphs. Tree++ [6] is proposed for the problem of comparing graphs at multiple different scales. It counts the number of occurrences of path-patterns in graphs.…”

Section: A Graph Kernelsmentioning

confidence: 99%

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Graph Kernels Based on Multi-scale Graph Embeddings

Ye¹,

Tian²,

Chen³

2022

Preprint

Self Cite

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Graph kernels are conventional methods for computing graph similarities. However, most of the R-convolution graph kernels face two challenges: 1) They cannot compare graphs at multiple different scales, and 2) they do not consider the distributions of substructures when computing the kernel matrix. These two challenges limit their performances. To mitigate the two challenges, we propose a novel graph kernel called the Multiscale Path-pattern Graph kernel (MPG), at the heart of which is the multi-scale path-pattern node feature map. Each element of the path-pattern node feature map is the number of occurrences of a path-pattern around a node. A path-pattern is constructed by the concatenation of all the node labels in a path of a truncated BFS tree rooted at each node. Since the path-pattern node feature map can only compare graphs at local scales, we incorporate into it the multiple different scales of the graph structure, which are captured by the truncated BFS trees of different depth. We use the Wasserstein distance to compute the similarity between the multi-scale path-pattern node feature maps of two graphs, considering the distributions of substructures. We empirically validate MPG on various benchmark graph datasets and demonstrate that it achieves state-of-the-art performance.

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Tree++: Truncated Tree Based Graph Kernels

Cited by 14 publications

References 27 publications

Ordinal Pattern Kernel for Brain Connectivity Network Classification

Ordinal Pattern Kernel for Brain Connectivity Network Classification

Nadege: When Graph Kernels meet Network Anomaly Detection

Graph Kernels Based on Multi-scale Graph Embeddings

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