Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. In reality, when users encounter a piece of information, such as a smartphone product, they often associate it with related products in their minds, such as earphones or computers from the same brand. Additionally, information platforms frequently recommend related content to users, amplifying this cascading effect and leading to multiplex influence diffusion. This paper first formulates the Multiplex Influence Maximization (Multi-IM) problem using multiplex diffusion models with an information association mechanism. In this problem, the seed set is a combination of influential users and information. To effectively manage the combinatorial complexity, we propose Graph Bayesian Optimization for Multi-IM (GBIM). The multiplex diffusion process is thoroughly investigated using a highly effective global kernelized attention message-passing module. This module, in conjunction with Bayesian linear regression (BLR), produces a scalable surrogate model. A data acquisition module incorporating the exploration-exploitation trade-off is developed to optimize the seed set further. Extensive experiments on synthetic and real-world datasets have proven our proposed framework effective. The code is available at https://github.com/zirui-yuan/GBIM.

As many real-world networks evolve over time, such as social networks, user-item networks, and IP-IP networks, anomaly detection for dynamic graphs has attracted growing attention. Most existing studies focus on detecting anomalous nodes or edges but fail to detect anomalous motif instances. In this paper, we propose MADG, a general Motif-level Anomaly Detection framework for dynamic Graphs, which can identify the anomaly in different motifs. Motifs are specific subgraph structures that frequently occur in a network and have been widely used in network analysis. In order to learn discriminative motif-level representations and leverage the temporal information from the dynamic graph, we design motif-augmented GCN and temporal self-attention. We first use motif-augmented GCN to model the topological structure among nodes and motif instances to learn their representations at each snapshot. Then, we feed the representations of multiple snapshots into the self-attention layer with relative temporal encoding in order to capture the evolutionary patterns. Extensive experiments on real-world dynamic graph datasets demonstrate the effectiveness of our proposed MADG framework.