A Lightweight Heterogeneous Graph Embedding Framework for Hotspot Detection

With the development and popularity of social networks, many human beings prefer to share their experiences on these networks. There are various methods proposed by the researcher which utilized user-generated content in the location-based social networks (LBSN) and recommend locations to users. However, there is a high sparsity in the user check-in information makes it tough to recommend the appropriate and accurate location to the user. To fix this issue, we put forward a proposal as a framework which utilizes a wide range of information available in these networks, each of which has its own type and provides appropriate recommendation. For this purpose, we encode the information as a number of entities and its attributes in the form of a heterogeneous graph, then graph embedding methods are used to embed all nodes in unified semantic representation space. As a result, we are able to model relations between users and venues in an efficient way and ameliorate the accuracy of the proposed method that recommends a place to a user. Our method is implemented and evaluated using Foursquare dataset, and the evaluation results depict that our work, boost performance in terms of precision, recall, and f-measure compared to the baseline work.

Heterogeneous graphs can represent real-world problems in a way close to reality, supporting diverse types of vertices and edges. However, their inherent heterogeneity poses challenges in interpreting problem semantics. To address this, heterogeneous graph embedding, aiming to map graph elements to low-dimensional vectors, simplifies subsequent machine learning analysis. This approach has gained prominence in machine learning, fueling classification, recommendation, and similarity search applications. Embedding diverse data is essential for efficient data processing. Incorporating language models, like BERT, into heterogeneous graphs enhances semantic context capture, which is particularly useful when one vertex type represents text. Language models stand out in contextual representation, enriching graph vertex embeddings for various tasks. This paper proposes a novel approach to enhancing heterogeneous graph embeddings by combining language models and task class data. Our approach increases vector quality, accounting for graph structure, semantic textual information, and task labels. We compared our proposal with a language model in the aspect-based sentiment analysis task, demonstrating competitive results and, in some cases, a slight superiority. Furthermore, we explore applications of embeddings from auxiliary vertices in another task, highlighting another advantage of the approach over the language model.

DyHDGE: Dynamic heterogeneous transaction graph embedding for safety-centric fraud detection in financial scenarios

As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants.These online activities raise significant social concerns that require immediate action.Existing approaches to combating this challenge are generally impractical due to the imbalance of classes and scarcity of labeled samples in realworld applications.To this end, we propose a novel Large Language Model-empowered Heterogeneous Graph Prompt Learning framework for illicit Drug Trafficking detection, called LLM-HetGDT, that leverages LLM to facilitate heterogeneous graph neural networks (HGNNs) to effectively identify drug trafficking activities in the class-imbalanced scenarios.Specifically, we first pre-train HGNN over a contrastive pretext task to capture the inherent node and structure information over the unlabeled drug trafficking heterogeneous graph (HG).Afterward, we employ LLM to augment the HG by generating high-quality synthetic user nodes in minority classes.Then, we finetune the soft prompts on the augmented HG to capture the important information in the minority classes for the downstream drug trafficking detection task.To comprehensively study online illicit drug trafficking activities, we collect a new HG dataset over Twitter, called Twitter-HetDrug.Extensive experiments on this dataset demonstrate the effectiveness, efficiency, and applicability of LLM-HetGDT.Our source code and dataset are available here.

On connections between Rényi entropy Principal Component Analysis, kernel learning and graph embedding

With integrated circuits shrinking in feature size, layout printability has become increasingly challenging, making lithographic hotspot detection ever-crucial in computer-aided design (CAD) flows. In recent years, numerous studies have explored deep learning to detect lithographic hotspots, offering promising results. However, neural networks can easily be biased and overfit when lacking sufficient training data, especially in the CAD domain. A generalizable DL-based hotspot detector should learn the genuine lithography principle and ensure consistent accuracy across layouts from various designs at the same technology node, regardless of their varying design styles. However, we find that existing convolutional neural network (CNN)-based hotspot detectors fail to generalize to different circuit layouts other than the design it has been trained for. To this end, we propose a few-shot learning-based framework for generalizable CNN-based hotspot detection. We develop a meta-learning scheme that asynchronously updates the CNN feature extraction and classification component to obtain a metainitialized model that can quickly adapt to new designs using as few as one training layout clip. We propose a layout topologybased sampling strategy for few-shot adaptation to enhance generalization stability. Experimental results on ICCAD 2012 and 2019 datasets show that our framework enables superior generalization capabilities than prior arts on unseen new designs.

Heterogeneous graph embedding by aggregating meta-path and meta-structure through attention mechanism

TopoDetect: Framework for topological features detection in graph embeddings

Real-world knowledge is growing rapidly nowadays. New entities arise with time, resulting in large volumes of relations that do not exist in current knowledge graphs (KGs). These relations containing at least one new entity are called emerging relations. They often appear in news, and hence the latest information about new entities and relations can be learned from news timely. In this paper, we focus on the problem of discovering emerging relations from news. However, there are several challenges for this task: (1) at the beginning, there is little information for emerging relations, causing problems for traditional sentence-based models; (2) no negative relations exist in KGs, creating difficulties in utilizing only positive cases for emerging relation detection from news; and (3) new relations emerge rapidly, making it necessary to keep KGs up to date with the latest emerging relations. In order to address these issues, we start from a global graph perspective and propose a novel Heterogeneous graph Embedding framework for Emerging Relation detection (HEER) that learns a classifier from positive and unlabeled instances by utilizing information from both news and KGs. Furthermore, we implement HEER in an incremental manner to timely update KGs with the latest detected emerging relations. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed HEER model.

GBRM: a graph embedding and blockchain-based resource management framework for 5G MEC

HGE-BVHD: Heterogeneous graph embedding scheme of complex structure functions for binary vulnerability homology discrimination

RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism

Detection of hate speech in multilingual as well as code-switched settings continues to remain a major challenge for online content moderation. This paper presents a new method that employs Language-Agnostic SEntence Representations (LASER) embeddings with token-level language identification along with adapter layers for efficient hate speech identification in 13 languages. Our method provides three contributions: (1) token-level language identification to analyze code-switching behavior, (2) light-weight adapter layers for per-language few-shot learning with no loss of multilingual representation, as well as (3) an explanation module with clear decision-making. We experiment on a multilingual collection of 219,981 samples, which shows that our new model attains an F1-score of 0.7337 as well as AUC-ROC of 0.8802 with a 0.65 percentage point improvement over a baseline. Token-level analysis shows that 60.78% of text samples display code-switching behavior, indicating a need for fine-grain language identification. Our explanation study shows that LASER embeddings contribute 3.5 times more than language features towards final prediction. The generalizability of the model extends over a large range of languages that cover European, Asian, as well as low-resource languages, with good cross-lingual performance with hate detection rates between 20.10% as well as 48.71% across test languages.

Cross-domain vulnerability detection using graph embedding and domain adaptation

Graph mining tasks often suffer from the lack of supervision from labeled information due to the intrinsic sparseness of graphs and the high cost of manual annotation. To alleviate this issue, inspired by recent advances of self-supervised learning (SSL) on computer vision and natural language processing, graph self-supervised learning methods have been proposed and achieved remarkable performance by utilizing unlabeled information. However, most existing graph SSL methods focus on homogeneous graphs, ignoring the ubiquitous heterogeneity of real-world graphs where nodes and edges are of multiple types. Therefore, directly applying existing graph SSL methods to heterogeneous graphs can not fully capture the rich semantics and their correlations in heterogeneous graphs. In light of this, we investigate self-supervised learning on heterogeneous graphs and propose a novel model named Multi-View Self-supervised heterogeneous graph Embedding (MVSE). By encoding information from different views defined by meta-paths and optimizing both intra-view and inter-view contrastive learning tasks, MVSE comprehensively utilizes unlabeled information and learns node embeddings. Extensive experiments are conducted on various tasks to show the effectiveness of the proposed framework.