Graph self-supervised learning (GSSL) alleviates the graph data labeling bottleneck without supervision, enabling wide application in domains like recommendation systems and social network analysis. High-frequency signals are valuable in GSSL for capturing local structural preferences, thereby enriching graph representations and boosting model performance. However, in practical applications, two critical problems hinder the efficient and robust use of these signals. First, the locality of high-frequency signals limits their full utilization by the model. Second, over-reliance on specific high-frequency signals will affect the model's generalization. To address the above problems, we propose the Frequency-Corrupt Based Graph Self-Supervised Learning (FC-GSSL) algorithm. Specifically, we generate corrupted graphs biased toward high-frequency signals by corrupting nodes and edges according to their low-frequency contributions. These corrupted graphs are fed as input to an autoencoder, with low-frequency and general features serving as the supervision. This compels the model to effectively fuse high- and low-frequency signals, thereby integrating and utilizing more valuable high-frequency information. Additionally, we design multiple sampling strategies and form diverse corrupted graphs based on the intersections and union between the results obtained from these strategies. By aligning the node representations from these views, the model can identify valuable frequency combinations, which helps reduce the negative impact of specific high-frequency components and improve generalization. FC-GSSL optimizes the design of GSSL for web applications, significantly improving model performance on complex web-related graphs, such as social networks and citation networks. This work makes a direct contribution to advancing the ''Graph Algorithms and Modeling for the Web'' research track. Experimental results on 14 datasets across multiple tasks illustrate the superiority of the proposed approach.

Multimodal embeddings of items and users, along with the loss function in the prediction model, are crucial for multimodal recommendation. Previous studies focused on feature extraction and fusion across modalities but lacked collaborative optimization of modal features across different association graphs. While prediction models address data alignment and user preference enhancement, they overlook preference consistency and varying contributions of different modalities to recommendations. In light of these issues, this paper proposes a multimodal recommendation method that combines Modality Shared Encoding with Multidimensional Loss optimization (MSE-ML). We introduce a multi-view feature joint encoding module, leveraging modality sharing mechanism to enable collaborative optimization of item embeddings across the user item graph and multiple modality-aware item association graphs. Additionally, we design a multidimensional loss optimization strategy that simultaneously promotes modality alignment, preserves preference consistency, and enhance the contribution of weak modality. Experimental results on publicly available datasets show that MSE-ML outperforms state-of-the-art approaches in terms of recommendation quality.

Software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

The purpose of Intrusion Detection Systems (IDS) is to identify security issues in data transmitted by various devices and communication protocols. For domains with sparse data, such as the Internet of Things (IoT), cross-domain models are applied to solve the sparse problem by transfer knowledge from the source domain with rich data to the target domain. However, most of the cross-domain intrusion detection methods map different explicit features in the source and target domains to implicit features in a common implicit space, which weakens the interpretability of these methods. To enhance the interpretability of cross-domain models, we propose a Cross-Domain Intrusion Detection Method Based on Nonlinear Augmented Explicit Features (NAEF). Specifically, we augment the feature space of the source and target domains as the combination of shared features, source domain specific features and target domain specific features. Moreover, we model the nonlinear mapping relationship from shared features to special features in the source and target domains separately. Then, the original features in the source and target domains are mapped to uniform explicit features in the augmented space by migration of the nonlinear mapping relationship. Additionally, a classifier based on ensemble learning and attention mechanism balances the data distribution and selects important features to enhance detection performance. Our experimental results demonstrate the effectiveness of the proposed NAEF method on four public datasets.

Obtaining high-quality service function vectors and aggregating neighborhood features in service association graph are prevalent methods for Mashup service clustering. However, existing methods often focus on enhancing the service functional feature extraction while overlooking distinctions among different services when creating service function vectors. Additionally, neighborhood feature aggregation is typically considered within a single association graph, lacking contrast optimization of different association features. To address these challenges, we propose a novel framework, MSCCL (Mashup Service Clustering with Contrastive Learning). MSCCL consists of two core components: a service function vector generation module and a neighborhood feature aggregation module. Contrastive learning is employed to enhance vector quality and optimize feature aggregation in both modules. We present a service clustering method within MSSCL that combines techniques from BERT (Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks). Compared to state-of-the-art methods, this approach reduces DBI by 2.03% to 12.58%, while enhancing SC, NMI, and Purity by 2.24% to 15.47%, 3.34% to 11.39%, and 2.58% to 13.65%, respectively. Furthermore, the experiments demonstrate that the popular models for service function vector generation and neighborhood feature aggregation can all be integrated into MSSCL. After being integrated into MSSCL, the clustering performance of these models was significantly improved, highlighting the effectiveness and generalizability of MSSCL.

Summary Petri nets are frequently used formal tools to analyze the compatibility of interactive service processes described by Web Services Business Process Execution Language (WS‐BPEL). However, the traditional methods based on Petri nets were with a high computable complexity for state space explosion. To resolve such problem, a logic Petri net–based path detecting method for compatibility analysis of interactive service processes is proposed. From the provided mapping rules, the service process described by WS‐BPEL is modeled as a service net based on logic Petri nets. The evaluation of interactive compatibility of two service processes is converted to analyze whether their service nets can be composed as a non‐blocked synthetic service net. The non‐blocked property is checked by detecting the reachability of the potential connected paths in a service net. To reduce the complexity of computing the connected paths in a service net, we propose a merge‐reduced method to generate the path expression of its skeleton service net. The potential connected paths of a service net can be obtained by unfolding the path expression. Compared with the traditional method based Petri nets, the proposed method is with high efficiency and it can greatly alleviate the problem of state space explosion in analyzing interactive compatibility of service processes.

Summary Alignment is the state‐of‐the‐art technique in conformance checking and becoming more important for the analysis of business processes. To improve the efficiency of alignment, a new alignment approach is presented based on Petri net models and traces. It takes artificial logs and models as an example to illustrate the procedure of the new alignment approach. The approach can generate an optimal alignment tree including all of the optimal alignments between the given trace and the Petri net model based on standard likelihood cost function. This paper gives the approach a specific and rigorous characterization. The approach is implemented on ProM as a plugin and has been evaluated using complex logs and models as a case study.