DGCNN: A convolutional neural network over large-scale labeled graphs

Phan, Anh Viet; Nguyen, Minh Le; Nguyen, Yen Lam Hoang; Bui, Lam Thu

doi:10.1016/j.neunet.2018.09.001

Cited by 184 publications

(52 citation statements)

References 6 publications

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“…( 3) where encodes the weights and denotes the Euclidean inner product. In addition to considering the features of neighbors , the third method [23] inspired by the above two methods uses the pairwise Euclidean distance between features as the weights of aggregation operation: (4) and (5) where is a Gaussian kernel.…”

Section: Local Geometric Relation (Lgr) Module For Local Feature Extrmentioning

confidence: 99%

“…The fourth method [3] computes the graph by using nearest neighbors in the feature space and combines neighbor's feature and neighborhood relation information :…”

Section: Local Geometric Relation (Lgr) Module For Local Feature Extrmentioning

confidence: 99%

“…To verify the reliability of the network, the sampled points of the resolution [24,25,26] respectively input into the trained network. The experimental results are summarized in Figure 4, where the compared networks are PointNet [1], PointNet++ [2], DGCNN [3], PCNN [23], RS-CNN [25]. The rough local structure of low-resolution point clouds increases the difficulty of local feature description.…”

Section: Classificationmentioning

confidence: 99%

“…Different from the encoding of traditional geometric hand-craft features, the key to neural networks is to fit a nonlinear function for feature extraction. Existing works like PointNet [1], PointNet++ [2], and DGCNN [3] have achieved remarkable results in feature extraction of 3D point clouds. However, these methods often set the model orientation as a priori, so they fail to analyze rotated points shape, and there are three main difficulties for point cloud as training data.…”

Section: Introductionmentioning

confidence: 99%

“…The graph neural network iteratively updates the state information of the nodes and the final state is used as a label for semantic segmentation. DGCNN [3] using the KNN algorithm finds neighbors of points in the feature space produced in each layer and takes feature difference as the relation between the nearest neighbors and the "center" point. RandLA-Net [20] proposes a local feature enhancement module, which extracts local geometric structures by increasing the visible area of each point.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Local Feature Extraction Network for Point Cloud Analysis

Zhou¹,

Tai²,

Chen³

et al. 2021

Symmetry

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Geometric feature extraction of 3D point clouds plays an important role in many 3D computer vision applications such as region labeling, 3D reconstruction, object segmentation, and recognition. However, hand-designed features on point clouds lack semantic information, so cannot meet these requirements. In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. LFE-Net consists of local geometric relation (LGR) module which aims to learn a high-dimensional local feature to express the relation between points and their neighbors. Benefiting from the additional singular values of local points and hierarchical neural networks, the learned local features are robust to permutation and rigid transformation so that they can be transformed into 3D descriptors. Moreover, we embed prior spatial information of the local points into the sub-features for combining features from multiple levels. LFE-Net achieves state-of-the-art performances on standard benchmarks including ModelNet40, ShapeNetPart.

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Section: Local Geometric Relation (Lgr) Module For Local Feature Extrmentioning

confidence: 99%

“…The fourth method [3] computes the graph by using nearest neighbors in the feature space and combines neighbor's feature and neighborhood relation information :…”

Section: Local Geometric Relation (Lgr) Module For Local Feature Extrmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Feature Extraction Network for Point Cloud Analysis

Zhou¹,

Tai²,

Chen³

et al. 2021

Symmetry

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Malware detection with dynamic evolving graph convolutional networks

Zhang

Wang

et al. 2022

Int J of Intelligent Sys

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Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats come from a small number of Application Programming Interfaces (APIs) embedded in the API sequences, which are easily ignored or obfuscated in the detection process.Prior works proposed graph-based learning methods to solve this problem using API-level behavior relations.However, the malware detection is still challenging, due to the ignore of the temporal correlation between malicious behaviors. In this study, we model the software behaviors with multiscaled API graph sequences to represent API-level behaviors as well as graph-level temporal behavior correlations. We then propose a novel Dynamic Evolving Graph Convolutional Network (DEGCN) model to capture dynamic evolving pattern of both local API-level and global graph-level software behaviors. In particular, we first extract the API-level (node) representations to capture the directed graph representations for each time slot. We then propose a Graph-encoding-based Gate Recurrent Unit (GGRU) network to capture the graph-level evolving features and their evolving status. The graph features of different time slots and different graph scales are concatenated to detect whether the software is benign or malicious. Our evaluation with two public

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