Thus, emerging network science has become an important research field. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.Īt present, various complex network structures have been integrated into our life, such as transportation networks, computer networks, citation networks, and so on. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. # Linux/i386 5.17.0-rc1 Kernel ConfigurationĬONFIG_CC_VERSION_TEXT="gcc-9 (Debian 9.3.0-22) 9.3.In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. # Automatically generated file DO NOT EDIT. # please remove ~/.lkp and /lkp dir to run from a clean state.ĠDAY/LKP+ Test Infrastructure Open Source Technology Intel Corporation # if come across any failure that blocks the test, EIP: _stack_depot_save (lib/stackdepot.c:396) Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.12.0-1 #PF: error_code(0x0000) - not-present page #PF: supervisor read access in kernel mode BUG: unable to handle page fault for address: 003b2aa0 If you fix the issue, kindly add following tag | Kernel_panic-not_syncing:Fatal_exception | 0 | 10 | | BUG:unable_to_handle_page_fault_for_address | 0 | 10 | On test machine: qemu-system-x86_64 -enable-kvm -cpu SandyBridge -smp 2 -m 16GĬaused below changes (please refer to attached dmesg/kmsg for entire log/backtrace): 14:54 ` Hyeonggon Yoo 0 siblings, 1 reply 10+ messages in threadįrom: kernel test robot 2:15 UTC ( / raw)įYI, we noticed the following commit (built with gcc-9):Ĭommit: ae107fa91914f098cd54ab77e68f83dd6259e901 ("mm/slub: use stackdepot to save stack trace in objects") Ae107fa919: BUG:unable_to_handle_page_fault_for_address LKML Archive on help / color / mirror / Atom feed * ae107fa919: BUG:unable_to_handle_page_fault_for_address 2:15 kernel test robot
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