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1. Modeling Co-evolution of Attributed and Structural Information in Graph Sequence

   https://doi.org/10.1109/TKDE.2021.3094332  

   Wang, Daheng; Zhang, Zhihan; Ma, Yihong; Zhao, Tong; Jiang, Tianwen; Chawla, Nitesh; Jiang, Meng (July 2021, IEEE Transactions on Knowledge and Data Engineering)

   null (Ed.)

   Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attributed graphs exhibit complex phenomenon of co-evolution between node attributes and graph structure. Learning node embeddings for forecasting change of node attributes and evolution of graph structure over time remains an open problem. In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence. It preserves the impact of earlier graphs on the current graph by embedding generation through the sequence of attributed graphs. It has a temporal self-attention architecture to model long-range dependencies in the evolution. Moreover, CoEvoGNN optimizes model parameters jointly on two dynamic tasks, attribute inference and link prediction over time. So the model can capture the co-evolutionary patterns of attribute change and link formation. This framework can adapt to any graph neural algorithms so we implemented and investigated three methods based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the framework (and its methods) outperforms strong baseline methods on predicting an entire unseen graph snapshot of personal attributes and interpersonal links in dynamic social graphs and financial graphs. 

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2. Topical network embedding

   https://doi.org/10.1007/s10618-019-00659-7  

   Shi, Min; Tang, Yufei; Zhu, Xingquan; Liu, Jianxun; He, Haibo (October 2019, Data Mining and Knowledge Discovery)

   Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to all words or attributes. In this paper, we argue that considering topic-level semantic interactions between nodes is crucial to learn discriminative node embedding vectors. In order to model pairwise topic relevance between linked text nodes, we propose topical network embedding, where interactions between nodes are built on the shared latent topics. Accordingly, we propose a unified optimization framework to simultaneously learn topic and node representations from the network text contents and structures, respectively. Meanwhile, the structure modeling takes the learned topic representations as conditional context under the principle that two nodes can infer each other contingent on the shared latent topics. Experiments on three real-world datasets demonstrate that our approach can learn significantly better network representations, i.e., 4.1% improvement over the state-of-the-art methods in terms of Micro-F1 on Cora dataset. (The source code of the proposed method is available through the github link: https:// github.com/codeshareabc/TopicalNE.) 

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3. Partial trajectory method to align and validate successive video cameras for vehicle tracking

   https://doi.org/10.1016/j.trc.2023.104416  

   Coifman, Benjamin; Li, Lizhe (January 2024, Transportation Research Part C: Emerging Technologies)

   This paper develops the partial trajectory method to align the views from successive fixed cameras that are used for video-based vehicle tracking across multiple camera views. The method is envisioned to serve as a validation tool of whatever alignment has already been performed between the cameras to ensure high fidelity with the actual vehicle movements as they cross the boundaries between cameras. The strength of the method is that it operates on the output of vehicle tracking in each camera rather than secondary features visible in the camera view that are unrelated to the traffic dynamics (e.g., fixed fiducial points). Thereby providing a direct feedback path from the tracking to ensure the quality of the alignment in the context of the traffic dynamics. The method uses vehicle trajectories within successive camera views along a freeway to deduce the presence of an overlap or a gap between those cameras and quantify how large the overlap or gap is. The partial trajectory method can also detect scale factor errors between successive cameras. If any error is detected, ideally one would redo the original camera alignment, if that is not possible, one could use the calculations from the algorithm to post hoc address the existing alignment. This research manually re-extracted the individual vehicle trajectories within each of the seven camera views from the NGSIM I-80 dataset. These trajectories are simply an input to the algorithm. The resulting method transcends the dataset and should be applicable to most methods that seek to extract vehicle trajectories across successive cameras. That said, the results reveal fundamental errors in the NGSIM dataset, including unaccounted for overlap at the boundaries between successive cameras, which leads to systematic speed and acceleration errors at the six camera interfaces. This method also found scale factor errors in the original NGSIM homographies. In response to these findings, we identified a new aerial photo of the NGSIM site and generated new homographies. To evaluate the impact of the partial trajectory method on the actual trajectory data, the manually re-extracted data were projected into the new coordinate system and smoothed. The re-extracted data shows much greater fidelity to the actual vehicle motion. The re-extracted data also tracks the vehicles over a 14% longer distance and adds 23% more vehicles compared to the original NGSIM dataset. As of publication, the re-extracted data from this paper will be released to the research community. 

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4. Unsupervised Attributed Network Embedding via Cross Fusion

   https://doi.org/10.1145/3437963.3441763  

   Pan, Guosheng; Yao, Yuan; Tong, Hanghang; Xu, Feng; Lu, Jian (March 2021, WSDM)

   null (Ed.)

   Attributed network embedding aims to learn low dimensional node representations by combining both the network's topological structure and node attributes. Most of the existing methods either propagate the attributes over the network structure or learn the node representations by an encoder-decoder framework. However, propagation based methods tend to prefer network structure to node attributes, whereas encoder-decoder methods tend to ignore the longer connections beyond the immediate neighbors. In order to address these limitations while enjoying the best of the two worlds, we design cross fusion layers for unsupervised attributed network embedding. Specifically, we first construct two separate views to handle network structure and node attributes, and then design cross fusion layers to allow flexible information exchange and integration between the two views. The key design goals of the cross fusion layers are three-fold: 1) allowing critical information to be propagated along the network structure, 2) encoding the heterogeneity in the local neighborhood of each node during propagation, and 3) incorporating an additional node attribute channel so that the attribute information will not be overshadowed by the structure view. Extensive experiments on three datasets and three downstream tasks demonstrate the effectiveness of the proposed method. 

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5. Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

   https://doi.org/10.1145/3366423.3380149  

   Sun, Yiwei; Wang, Suhang; Tang, Xianfeng; Hsieh, Tsung-Yu; Honavar, Vasant G. (January 2020, Proceedings of The Web Conference 2020 (WWW '20))

   Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world applications. In contrast, it is much more expedient on the part of the attacker to inject adversarial nodes, e.g., fake profiles with forged links, into existing graphs so as to reduce the performance of the GNN in classifying existing nodes. Hence, we consider a novel form of node injection poisoning attacks on graph data. We model the key steps of a node injection attack, e.g., establishing links between the injected adversarial nodes and other nodes, choosing the label of an injected node, etc. by a Markov Decision Process. We propose a novel reinforcement learning method for Node Injection Poisoning Attacks (NIPA), to sequentially modify the labels and links of the injected nodes, without changing the connectivity between existing nodes. Specifically, we introduce a hierarchical Q-learning network to manipulate the labels of the adversarial nodes and their links with other nodes in the graph, and design an appropriate reward function to guide the reinforcement learning agent to reduce the node classification performance of GNN. The results of the experiments show that NIPA is consistently more effective than the baseline node injection attack methods for poisoning graph data on three benchmark datasets. 

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