Graph-based Approach Research Articles

Improving quality of indicators of compromise using STIX graphs

Cybersecurity relies on Indicators of Compromise (IoCs) to detect and address threats. Although Threat Intelligence Platforms (TIPs) and Open Source Intelligence (OSINT) are common sources for gathering IoCs, their reliability varies. In our study, we enhance the management of IoCs and OSINT by introducing a novel method that reliably assesses IoC’s threat severity and confidence scores, focusing on Structured Threat Information eXpression (STIX) for threat associations. Our approach, implemented on OpenCTI, significantly enhances IoC value, as it aggregates threat intelligence from diverse sources utilizing a STIX graph-based approach, which is a unique feature among TIPs. Additionally, our method employs heuristic analysis to optimize IoC scoring. It takes into account factors such as relevance, completeness, timeliness, accuracy, and consistency while emphasizing the confidence of the source. Notably, the proposed method has enhanced the precision of the confidence score, achieving a 25.18% reduction in the average difference of confidence scores compared to the benchmarked platform. The Emotet and Medusa case studies underscore the importance of source credibility in confidence scores, emphasizing our TIP’s precision in cybersecurity threat assessment and defense enhancement.

Joint Projected Fuzzy Neighborhood Preserving C-means Clustering with Local Adaptive Learning

Graph-based dimensionality reduction approaches are extensively applied for preserving the intrinsic structures of high-dimensional data. To capture discriminative information from data clusters that lack a clear manifold structure, previous studies have combined clustering algorithms with local manifold structure preservation. These methods assume that two neighbor data points share the same cluster label. However, the assumption may not hold for Fuzzy C-Means which may assign two nearby data points to different clusters depending on the distance between data points and cluster centroids. This inconsistency degrades the performance in clustering and dimensionality reduction. To tackle this issue, we propose a novel approach for dimensionality reduction, termed joint Projected Fuzzy Neighborhood Preserving C-means Clustering with Local Adaptive Learning (PFNPCM). PFNPCM learns cluster centroids, sparse membership grades, embedded subspaces, and structured graphs simultaneously and extracts both global and local structures of data. Through clustering data that preserves the local neighborhood structure in a low-dimensional space instead of the original one, PFNPCM can effectively capture both global and local information from the inherent manifold structure and cluster structure. The employment of ℓ2,1-norm as the distance metric in the clustering and the preservation of neighborhood structure further ensure the robustness of clustering results against noise or outliers. To achieve the consistency of the captured global and local discriminative information, PFNPCM adopts a novel fuzzy local similarity measure based on the manifold structure, and the manifold assumption thus holds when partitioning unlabeled samples into subgroups. Massive experiments on diverse well-known datasets demonstrate that PFNPCM is superior to some state-of-the-art methods.

Enhancing Hansen Solubility Predictions with Molecular and Graph-Based Approaches

The fast and accurate prediction of Hansen solubility benefits many diverse fields such as pharmaceuticals, the food industry, and cosmetics. To estimate the individual HSP values (polar, dispersive, and hydrogen bonding components), we investigated the performance of using Mordred descriptors in multiple linear regressions and XGBoost modeling. For HSP predictions, we also tested a graph-based molecular representation with graph neural network (GNN) modeling. To select the optimal models for final training and predictions, we used nested cross-validation and hyper-parameter optimization. The models with the best predictive performance were selected through internal (R2train, RMSE, MEPcv) and external (RMSEP, CCC, MEP, R2test, ar2m, Δr2m) validation metrics using ∼1200 compounds from free-available database https://www.stevenabbott.co.uk. To confirm the practical reliability, we examined the agreement of experimentally obtained HSP data from the literature for 93 compounds and the data predicted by the created models. The results of GNN modeling showed the best predictive characteristics, which include a coefficient of determination between experimentally obtained and predicted HSP values greater than 0.76 for polar and hydrogen bond forces and greater than 0.66 for dispersive forces. Interpreting the fundamental basis of Hansen solubility using the created MLR equations and XGBoost models, HSP values were found to be influenced by van der Waals volume characteristics, 2D matrix molecular representation, and polarity. We elaborated on the practical benefits of using the selected GNN method through Hansen's solubility sphere as an example. This is the first study to demonstrate the advantages of GNN in predicting individual HSP components, as well as the first study to describe in detail their molecular basis using MLR and XGBoost modeling.

A graph-based method for identifying critical pipe failure combinations in water distribution networks

ABSTRACT Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments.

Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks

Abstract: In this paper, we present VFFinder, a novel graph-based approach for automated silentvulnerability fix identification. To precisely capture the meaning of code changes, the changed code isrepresented in connection with the related unchanged code. In VFFinder, the structure of the changedcode and related unchanged code are captured and the structural changes are represented in annotatedAbstract Syntax Trees (αAST). VFFinder distinguishes vulnerability-fixing commits from non-fixingones using attention-based graph neural network models to extract structural features expressed inαASTs. We conducted experiments to evaluate VFFinder on a dataset of 11K+ vulnerability fixingcommits in 507 real-world C/C++ projects. Our results show that VFFinder significantly improvesthe state-of-the-art methods by 272–420% in Precision, 22–70% in Recall, and 3.2X–8.2X in F1.Especially, VFFinder speeds up the silent fix identification process by up to 121% with the sameeffort reviewing 50K LOC compared to the existing approaches.Keywords: Silent vulnerability fixes, vulnerability fix identification, code change representation,graph-based model.

A knowledge graph-based approach to modeling & representation for machining process design intent

Numerical control machining process design, which connects product design and modern manufacturing, is the extension of the design process in the process field. The process design intent, derived from in-depth part analysis, embodies the cognitive and strategic thinking of technologists in response to the specific design requirements during the process design. It encapsulates interpretable experiential knowledge and provides valuable insights for subsequent process design stages. However, the process design intent is the tacit experiential knowledge of technologists that implicitly exists in the process design, lacking a reasonable organizational framework and effective method to effectively represent and capture it. To fill this gap, a process design intent model for representing and capturing useful design intent knowledge for future reuse is developed and evaluated in this study. Firstly, this paper analyzes the existence of process design intent in process design and gives a brief description of the working scheme of the process design intent model. Then, the key modules of the model are introduced in detail and a knowledge graph of process design intent is built based on the model structure. Moreover, the process design intent annotator is developed to effectively capture process design intent. Finally, the example for capturing and representing process design intent is shown and the model is evaluated and discussed by the example.

AGAR - Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects

This article focuses on motion prediction for point cloud sequences in the challenging case of deformable 3D objects, such as human body motion. First, we investigate the challenges caused by deformable shapes and complex motions present in this type of representation, with the ultimate goal of understanding the technical limitations of state-of-the-art models. From this understanding, we propose an improved architecture for point cloud prediction of deformable 3D objects. Specifically, to handle deformable shapes, we propose a graph-based approach that learns and exploits the spatial structure of point clouds to extract more representative features. Then, we propose a module able to combine the learned features in a adaptative manner according to the point cloud movements. The proposed adaptative module controls the composition of local and global motions for each point, enabling the network to model complex motions in deformable 3D objects more effectively. We tested the proposed method on the following datasets: MNIST moving digits, the Mixamo human bodies motions [ 15 ], JPEG [ 5 ] and CWIPC-SXR [ 32 ] real-world dynamic bodies. Simulation results demonstrate that our method outperforms the current baseline methods given its improved ability to model complex movements as well as preserve point cloud shape. Furthermore, we demonstrate the generalizability of the proposed framework for dynamic feature learning by testing the framework for action recognition on the MSRAction3D dataset [ 19 ] and achieving results on par with state-of-the-art methods.

Global diversity, recurrent evolution, and recent selection on amylase structural haplotypes in humans.

The adoption of agriculture, first documented ~12,000 years ago in the Fertile Crescent, triggered a rapid shift toward starch-rich diets in human populations. Amylase genes facilitate starch digestion and increased salivary amylase copy number has been observed in some modern human populations with high starch intake, though evidence of recent selection is lacking. Here, using 52 long-read diploid assemblies and short read data from ~5,600 contemporary and ancient humans, we resolve the diversity, evolutionary history, and selective impact of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in populations with agricultural subsistence compared to fishing, hunting, and pastoral groups. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each exhibit multiple duplications/deletions with mutation rates >10,000-fold the SNP mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome graph-based approach to infer structural haplotypes across thousands of humans, we identify extensively duplicated haplotypes present at higher frequencies in modern day populations with traditionally agricultural diets. Leveraging 533 ancient human genomes we find that duplication-containing haplotypes (i.e. haplotypes with more amylase gene copies than the ancestral haplotype) have increased in frequency more than seven-fold over the last 12,000 years providing evidence for recent selection in West Eurasians. Together, our study highlights the potential impacts of the agricultural revolution on human genomes and the importance of long-read sequencing in identifying signatures of selection at structurally complex loci.

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.

Investigation of graph-based clustering approaches along with graph neural networks for modeling armed conflict in Bangladesh

Investigation of graph-based clustering approaches along with graph neural networks for modeling armed conflict in Bangladesh

A Joint Graph-Based Approach for Simultaneous Underwater Localization and Mapping for AUV Navigation Fusing Bathymetric and Magnetic-Beacon-Observation Data

A Joint Graph-Based Approach for Simultaneous Underwater Localization and Mapping for AUV Navigation Fusing Bathymetric and Magnetic-Beacon-Observation Data

Relation pruning and discriminative sampling over knowledge graph for long-tail recommendation

Long-tail recommendations have gained significant attention owing to their potential economic market. However, the scarcity of interaction data for long-tail users/items and the popularity bias present challenges in capturing high-quality embeddings for long-tail users/items. This predicament further exacerbates the long-tail recommendation problem, as current approaches tend to exhibit a bias towards making recommendations for short-head users/items, resulting in a detrimental cycle for long-tail recommendation. To this end, we propose a novel knowledge graph-based approach called LTailKG to improve long-tail recommendations. LTailKG leverages the semantic information in knowledge graph to produce high-quality embeddings and augmented samples for generating satisfactory long-tail recommendations. First, LTailKG parameterizes each node and relation as vector representations. Next, LTailKG presents a relation pruning-based graph contrastive learning operation to generate additional self-supervised signals for long-tail users/items, thereby producing high-quality embeddings for them. Furthermore, LTailKG introduces a knowledge graph-driven discriminative sampling operation to select augmented positive and negative samples from the uninteracted item set, which enables LTailKG to excel at not only identifying long-tail items that better align with the user's interests but also extracting the genuine preferences of long-tail users. Extensive experiments on real-world datasets demonstrate the superiority of LTailKG over state-of-the-art approaches in terms of long-tail recommendation.

A data-driven and knowledge graph-based analysis of the risk hazard coupling mechanism in subway construction accidents

With the development and expansion of subway construction, accidents usually cause economic losses and seriously threaten personnel safety. Exploring the experience and lessons from accident reports is beneficial for promoting subway construction safety management. Therefore, this study proposes a data-driven and knowledge graph-based approach to reveal the coupling mechanism of risk hazards in subway construction. A total of 231 subway construction accident reports in China from 2001 to 2023 are collected as the database of the knowledge graph. By developing a subway construction safety accident knowledge graph (SCSAKG), the potential paths for accidents can be revealed. To further investigate the internal features of the knowledge graph, topological indicators are proposed to explore the relationships between hazards, hazard types, hazard frequencies, hazard occurrence times, accidents, and accident consequences. Prevention strategies for key risk hazards in subway construction are developed based on the proposed coupling mechanism. The proposed methods that combine knowledge graphs with accident reports can provide decision-making references for subway construction safety management.

Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AI

Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AI

Report on the 1st International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) at ECIR 2024

The First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) was held as a physical (in-person) event on March 24, 2024, in conjunction with the 46th European Conference on Information Retrieval (ECIR 2024) in Glasgow (Scotland). The scientific program included paper, spotlight, and poster presentations. Two keynote talks were delivered by Francesco Fabbri (Spotify, Spain) and Ruihong Qiu (University of Queensland, Australia). This report presents an overview of the activities conducted during the workshop and the main topics covered. Date : 24 March 2024. Website : https://irongraphs.github.io/ecir2024/.

GraphPyRec: A novel graph-based approach for fine-grained Python code recommendation

Artificial intelligence has been widely applied in software engineering areas such as code recommendation. Significant progress has been made in code recommendation for static languages in recent years, but it remains challenging for dynamic languages like Python as accurately determining data flows before runtime is difficult. This limitation hinders data flow analysis, affecting the performance of code recommendation methods that rely on code analysis. In this study, a graph-based Python recommendation approach (GraphPyRec) is proposed by converting source code into a graph representation that captures both semantic and dynamic information. Nodes represent semantic information, with unique rules defined for various code statements. Edges depict control flow and data flow, utilizing a child-sibling-like process and a dedicated algorithm for data transfer extraction. Alongside the graph, a bag of words is created to include essential names, and a pre-trained BERT model transforms it into vectors. These vectors are integrated into a Gated Graph Neural Network (GGNN) process of the code recommendation model, enhancing its effectiveness and accuracy. To validate the proposed method, we crawled over a million lines of code from GitHub. Experimental results show that GraphPyRec outperforms existing mainstream Python code recommendation methods, achieving Top-1, 5, and 10 accuracy rates of 68.52%, 88.92%, and 94.05%, respectively, along with a Mean Reciprocal Rank (MRR) of 0.772.

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.

A Comparison Between A* and RRT Algorithm in Path Planning for Mobile Robot

The ability of mobile devices to navigate environments is vital. Navigation plays a critical role in averting collisions and hazardous situations.The design of paths is an essential component of robot navigation. comprising the ability to determine the optimal route from the robot's current location to a given destination. And in all state-of-the-art algorithms for robots to design the optimal route, A* and RRT algorithm stand out as the two most widely used path planning techniques, one is graph-based approach while the other is sample-based. In this paper, some variants or improvement of the two methods will be described. And a comparison about A* and RRT will be presented with appropriate criteria, which are path length and computational time. The main finding is that the A* method outperforms the Rapidly Exploring Random Tree (RRT) approach in terms of computational efficiency and path distance optimization. But it requires to consider more thoughts in applications. The comparison of the A* and RRT algorithms helps comprehend their applicability in various application circumstances. In this work, Evaluating these aspects altogether comprehensively provides a deeper understanding of the suitability of each algorithm for specific robotic navigation tasks.

Hierarchical Alignment with Polar Contrastive Learning for Next-Basket Recommendation

Next-basket recommendation methods focus on the inference of the next basket by considering the corresponding bas- ket sequence. Although many methods have been developed for the task, they usually suffer from data sparsity. The number of inter- actions between entities is relatively small compared to their huge bases, so it is crucial to mine as much hidden information as possible from the limited historical interactions for prediction. However, the existing methods mainly just treat the next-basket recommendation task as a single-view sequential prediction problem, which leads to the inadequate mining of the information hidden in multiple views, and the mining of other patterns in the historical interactions is neglected, thus making it difficult to learn high-quality representa- tions and limiting the recommendation effect. To alleviate the above issues, we propose a novel method named Hap CL for next-basket recommendation, which mines information from multiple views and patterns with the help of polar contrastive learning. A hier- archical module is designed to mine multiple patterns of historical interactions from different views at two levels. In order to mine self- supervised signals, we design a polar contrastive learning module with a novel graph-based augmentation approach. Experiments on three real-world datasets validate the effectiveness of Hap CL.

Trajectory-based clustering for enhanced attractive region mining in urban taxi services

ABSTRACT Trajectory data, increasingly available due to location tracking technologies, holds immense potential for intelligent traffic management and urban planning. Traditional 'attractive region' mining methods often rely on density-based clustering, neglecting the inherent path information within trajectories. To address this, we propose a novel graph-based approach for attractive region discovery. By transforming trajectory data into graphs, we effectively leverage path and connectivity information for clustering with locality-sensitive hashing. Our study introduces the pARM, pgARM, and hgARM algorithms, demonstrating their superiority over GridDBScan through experiments on real-world datasets. We employ Davies–Bouldin metric and visualization techniques to highlight the robustness of our approach, especially for datasets with varied degree distributions. Although our method may have slightly longer processing times for smaller grid sizes, it achieves execution times comparable to GridDBScan for larger grids. We rigorously analyze performance variations within our algorithms using execution time, clustering coefficient, and modularity scores, providing guidance for their optimal application.