Interactive Graphics Research Articles

Local and extensive fluctuations in sparsely interacting ecological communities.

Ecological communities with many species can be classified into dynamical phases. In systems with all-to-all interactions, a phase where species abundances always reach a fixed point and a phase where they continuously fluctuate have been found. The dynamics when interactions are sparse, with each species interacting with only a few others, has remained largely unexplored. Here we study a system of sparse interactions, first when interactions are of constant strength and completely unidirectional, and then when adding variability and bidirectionality. We show that in this case a phase unique to the sparse setting appears in the phase diagram, where for the same control parameters different communities may reach either a fixed point or a state where the abundances of only a finite subset of species fluctuate, and we calculate the probability for each outcome. These fluctuating species are organized around short cycles in the interaction graph, and their abundances undergo large nonlinear fluctuations. We characterize the approach from this phase to a phase with extensively many fluctuating species, and show that the probability of fluctuations grows continuously to one as the transition is approached, and that the number of fluctuating species diverges. This is qualitatively distinct from the transition to extensive fluctuations coming from a fixed point phase, which is marked by a loss of linear stability. The differences are traced back to the emergent binary character of the dynamics when far from short cycles.

Patient-patient interactions visualization for drug side effects in patients’ reviews

This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.

Autobalanced Multitask Node Embedding Framework for Intelligent Education.

Recently, online education has become popular. Many e-learning platforms have been launched with various intelligent services aimed at improving the learning efficiency and effectiveness of learners. Graphs are used to describe the pairwise relations between entities, and the node embedding technique is the foundation of many intelligent services, which have received increasing attention from researchers. However, the graph in the intelligent education scenario has three noteworthy properties, namely, heterogeneity, evolution, and lopsidedness, which makes it challenging to implement ecumenical node embedding methods on it. In this article, an autobalanced multitask node embedding model is proposed, named MNE, and applied to the interaction graph, settling a few actual tasks in intelligent education. More specifically, MNE builds two purpose-built self-supervised node embedding learning tasks for heterogeneous evolutive graphs. Edge-specific reconstruction tasks are built according to the semantic information and properties of the heterogeneous edges, and an evolutive weight regression task is designed, aiding the model to perceive the evolution of learners' implicit cognitive states. Then, both aleatoric and epistemic uncertainty quantification techniques are introduced, achieving both task- and node-level weight estimation and instructing subtask autobalancing. Experimental results on real-world datasets indicate that the proposed model outperforms the state-of-the-art graph embedding methods on two assessment tasks and demonstrates the validity of the proposed multitask framework and subtask balancing mechanism. Our implementations are available at https://github.com/ccnu-mathits/MNE4HEN.

Multi-space interaction learning for disentangled knowledge-aware recommendation

Knowledge graphs are becoming increasingly valuable as auxiliary information in current recommendation systems. User–item interaction modeling (within user–item interaction graphs) and knowledge association modeling (within knowledge graphs) play a vital role in representation learning. Most existing approaches are usually limited to modeling user–item interactions and knowledge associations in one space. However, they often exhibit distinctly non-Euclidean anatomy with diverse geometric features (e.g., trees and cyclic structures). In addition, models based on a single space cannot adequately accommodate multiple geometric patterns and ignore interaction factors between mixed geometries. To address these two issues, we utilize multi-space interactive learning for disentangled knowledge-aware recommendation (MKRec), which learns disentangled representations of users and items in multiple spaces, including hyperbolic, spherical, and Euclidean spaces, to preserve the geometric patterns of the data. Furthermore, to consider the interaction between mixed geometric shapes, we introduce an interactive learning mechanism along with a novel multi-space feature fusion method. Based on the empirical results, the evaluation of four datasets demonstrates the excellent efficacy of MKRec. In particular, our findings confirmed that the MKRec algorithm achieved significant results in disentangling user–item interactions and knowledge associations.

An Expense Tracker

Abstract: The Expense Tracker project aims to provide a user-friendly platform for individuals or households to track their expenses efficiently. In today's fast-paced world, managing finances can be challenging, and this project seeks to simplify the process by offering a comprehensive solution. The project offers a range of features to help users manage their expenses effectively. It allows users to record their daily expenses, categorize them into different categories such as food, transportation, bills, and entertainment, and set budget limits for each category. The system provides notifications when users exceed their budget limits, helping them stay on track with their financial goals. One of the key features of the Expense Tracker project is its ability to analyze spending patterns over time. Users can view their expense history through interactive charts and graphs, allowing them to identify trends and make informed decisions about their finances. This feature is particularly useful for budget planning and identifying areas where expenses can be reduced. Security is a top priority for the Expense Tracker project. The system uses secure user authentication to ensure that only authorized users have access to their financial data. All data is encrypted to protect it from unauthorized access, providing users with peace of mind about the security of their information.

A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption

Existing methods for optimizing the LLVM compiler face challenge in quickly obtaining high-quality solutions due to the large and discrete optimization space of option sequences, time-consuming energy consumption evaluation, and complex interactions among options. To address these challenges, we present a Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption (TSOMFEC). In the first stage, we propose an Option Selection algorithm based on Pattern Mining (OSPM). OSPM integrates a pattern mining algorithm and CRC algorithm to precisely preselect options, with the goal of reducing the search space of option sequences while guaranteeing optimization quality. In the second stage, we present an Option Sequence Evolutionary Algorithm (OSOA). OSOA encodes options and their positions in the sequence as individual items and then constructs an item interaction graph to capture complex interactions among different items. The item interaction graph is utilized in a genetic algorithm to reduce actual energy consumption evaluations and rapidly obtain high-quality option sequences. We evaluate the performance of TSOMFEC by comparing it with four state-of-the-art methods across eight typical embedded programs in terms of solution quality and optimization time. Furthermore, we validate the effectiveness of both OSPM and item interaction graph. The experimental results demonstrate that TSOMFEC not only achieves the best solution quality but also spends the least optimization time. It obtains average energy consumption improvement percentages relative to the -O3 level of 0.075, along with average optimization time of 3178 s. Moreover, the effectiveness of both OSPM and the item interaction graph has been validated.

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies

BackgroundThe identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects.ResultsTo address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features.ConclusionsHMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

Collect and Connect Data Leaves to Feature Concepts: Interactive Graph Generation Toward Wellbeing

Feature concepts and data leaves have been invented to foster thoughts for creating social and physical well-being through the use of datasets. The idea, simply put, is to at-tach selected and collected Data Leaves that are summaries of event flows to be discovered from corresponding datasets, on the target Feature Concept representing the expected scenarios of well-being individuals and well-being society. A graph of existing or expected datasets, attached in the form of Data Leaves on a Feature Concept, was generated semi-automatically. Rather than sheer auto-mated generative AI, our work addresses the process of generative artificial and natural intelligence to create the basis for collecting and connecting useful data.

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.

Light dual hypergraph convolution for collaborative filtering

Recommender systems filter information to meet users’ personalized interests actively. Existing graph-based models typically extract users’ interests from a heterogeneous interaction graph. They do not distinguish learning between users and items, ignoring the heterogeneous property. In addition, the interaction sparsity and long-tail bias issues still limit the recommendation performance significantly. Fortunately, hidden homogeneous correlations that have a considerable volume can entangle abundant CF signals. In this paper, we propose a light dual hypergraph convolution (LDHC) for collaborative filtering, which designs a hypergraph to involve heterogeneous and homogeneous correlations with more CF signals confronting the challenges. Over the integrated hypergraph, a two-level interest propagation is performed within the heterogeneous interaction graph and between the homogeneous user/item graphs to model users’ interests, where learning on users and items is distinguished and collaborated by the homogeneous propagation. Specifically, hypergraph convolution is lightened by removing unnecessary parameters to propagate users’ interests. Extensive experiments on publicly available datasets demonstrate that the proposed LDHC outperforms the state-of-the-art baselines.

Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning process.

With the rapid expansion of online education, there is a burgeoning interest within the EdTech space to offer tailored learning experiences that cater to individual student's abilities and needs. Within this framework, knowledge tracing tasks have garnered considerable attention. The primary objective of knowledge tracing is to develop a model that assesses a student's proficiency in a particular skill based on their historical performance in exercises, enabling predictions regarding the likelihood of correct responses in future exercises. While existing knowledge tracing models often incorporate information such as students' exercise answering history and skill mastery level, they frequently overlook the students' mental states during the learning process. This paper addresses this gap by introducing a novel psychological factors-enhanced heterogeneous learning interactive graph knowledge tracing model (Psy-KT). This model delineates the interactions among students, exercises, and skills through a heterogeneous graph, supplementing it with four psychological factors that capture students' mental states during the learning process: frustration level, confusion level, concentration level, and boredom level. In the modeling of students' learning processes, we incorporate the forgetting curve and construct relevant cognitive parameters from the features. Additionally, we employ the Item Response Theory (IRT) model to predict students' performance in answering exercises at the subsequent time step. This model not only delves into the psychological aspects of students during the learning process but also integrates the simulation of forgetting, a natural phenomenon in the learning journey. The inclusion of cognitive parameters enhances the description of changes in students' abilities throughout the learning process. This dual focus allows for a more comprehensive understanding of students' learning behaviors while providing a high level of interpretability for the model. Empirical validation of the Psy-KT model is conducted using four publicly available datasets, demonstrating its superior performance in predicting students' future performance. Through rigorous experimentation, the integration of psychological and forgetting factors in the Psy-KT model not only improves predictive accuracy but also enables educators to offer more targeted tutoring and advice, enhancing the overall efficacy of the learning experience.

Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution.

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism.

Approach to Detecting Malicious Bots in the Vkontakte Social Network and Assessing Their Parameters

The emergence of new varieties of bots in social networks and the improvement of their capabilities to imitate the natural behavior of real users represent a significant problem in the field of protection of social networks and online communities. This paper proposes a new approach to detecting and assessing the parameters of bots within the social network «VKontakte». The basis of the proposed approach is the creation of datasets using the method of «controlled purchase» of bots, which allows one to assess bots’ characteristics such as price, quality, and speed of action of bots, and using the Turing Test to assess how much users trust bots. In combination with traditional machine learning methods and features extracted from interaction graphs, text messages, and statistical distributions, it becomes possible to not only detect bots accurately but also predict their characteristics. This paper demonstrates that the trained machine learning model, based on the proposed approach, is robust to imbalanced data and can identify most types of bots as it has only a minor correlation with their main characteristics. The proposed approach can be used within the choice of countermeasures for the protection of social networks and for historical analysis, which allows not only to confirm the presence of bots but also to characterize the specifics of the attack.

Meta graph network recommendation based on multi-behavior encoding

As traditional recommendation systems ignore the hidden information among different user behaviors (such as clicks, add-to-favorites, add-to-cart, and purchases), this often leads to low accuracy in recommendation results. We propose a meta-graph network recommendation system via multi-behavior encoding (MBGR). Firstly, the graph convolutional neural network is used to extract features from various interactive behavior heterogeneous graphs of user-items for behavior heterogeneous modeling. Secondly, matrix decomposition algorithm and meta-knowledge learner are used respectively to process the semantic information of user behavior, and then attention mechanism is used to learn and distinguish the importance of different types of user item interaction behaviors. Finally, meta-knowledge transfer network is used to combine meta-learning paradigm and neural network framework to establish user target behavior recommendation. We conducted comparative experiments comparing MBGR with 7 different baseline models such as NCF and DMF. Extensive experiments on three real datasets (Tmall, Yelp, ML10M) demonstrate that the proposed MBGR method outperforms the baselines. The performance of MBGR is improved by 10.97 % on average with the metric of HR@10 and 10.96 % with the metric of NDCG@10. Under different top-N value evaluation conditions (HR@10, HR@7, NDCG@10, NDCG@7, etc.), the proposed model's performance can also be improved by more than 10 %, which proves the rationality and effectiveness of the proposed MBGR method.

Multiagent trajectory prediction with global‐local scene‐enhanced social interaction graph network

AbstractTrajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning‐based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real‐world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global‐local scene‐enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global‐local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios.

Deep Leaning for Malnutrition Detection: Implications for Global Health

Malnutrition continues to be a major global health concern, impacting millions of people from various demographic backgrounds. To lessen the grave health effects of malnutrition, early detection and focused intervention are essential. Conventional evaluation techniques, like physical examinations, body weight calculations, and blood testing, are frequently expensive, time-consuming, and unpredictable. This research describes a revolutionary machine learning strategy for successfully diagnosing malnutrition. Specifically, we leverage transfer learning approaches from pre-trained models and employ NumPy for linear algebra, pandas for data processing, and matplotlib for interactive graph plotting. Function approximation, prediction robustness, and classification accuracy are all improved by the ensemble framework. After processing the data set, our model divides it into four groups: normal, stunting, obesity, and wasting. The main goal is to accurately classify malnutrition so that customized therapies may be implemented for each group. By doing this, we hope to lower the risk of death, health issues, and physical disabilities brought on by malnutrition. In summary, our suggested approach marks a substantial development in the efficient identification of malnutrition and the provision of tailored care. Accurate classification is ensured with the incorporation of ensemble learning algorithms, opening the door for focused treatments to enhance patient outcomes. Keywords—Analysis, Machine learning, Ensemble learning, NumPy, Matplotlib, Seaborn, Malnutrition.

Interactive exploration of adverse events and multimorbidity in CKD.

Persons with chronic kidney disease (CKD) are at increased risk of adverse events, early mortality, and multimorbidity. A detailed overview of adverse event types and rates from a large CKD cohort under regular nephrological care is missing. We generated an interactive tool to enable exploration of adverse events and their combinations in the prospective, observational German CKD (GCKD) study. The GCKD study enrolled 5217 participants under regular nephrological care with an estimated glomerular filtration rate of 30-60 or >60mL/min/1.73m2 and an overt proteinuria. Cardio-, cerebro- and peripheral vascular, kidney, infection, and cancer events, as well as deaths were adjudicated following a standard operation procedure. We summarized these time-to-event data points for exploration in interactive graphs within an R shiny app. Multivariable adjusted Cox models for time to first event were fitted. Cumulative incidence functions, Kaplan-Meier curves and intersection plots were used to display main adverse events and their combinations by sex and CKD etiology. Over a median of 6.5 years, 10271 events occurred in total and 680 participants (13.0%) died while 2947 participants (56.5%) experienced any event. The new publicly available interactive platform enables readers to scrutinize adverse events and their combinations as well as mortality trends as a gateway to better understand multimorbidity in CKD: incident rates per 1000 patient-years varied by event type, CKD etiology, and baseline characteristics. Incidence rates for the most frequent events and their recurrence were 113.6 (cardiovascular), 75.0 (kidney), and 66.0 (infection). Participants with diabetic kidney disease and men were more prone to experiencing events. This comprehensive explorative tool to visualize adverse events (https://gckd.diz.uk-erlangen.de/), their combination, mortality, and multimorbidity among persons with CKD may manifest as a valuable resource for patient care, identification of high-risk groups, health services, and public health policy planning.

MD-GCCF: Multi-view deep graph contrastive learning for collaborative filtering

Collaborative Filtering (CF), a classical recommender system approach, learns users’ interests and behavioral preferences for items through a user–item interaction graph. CF based on graph neural network (GNN) and CF based on graph contrastive learning (GCL) show strong advantages in both modeling multi-layer signals and solving label sparsity, respectively. However, there are still two key problems to be solved: Most CF models based on (1) GNN suffer from the over-smoothing problem and are unable to aggregate deep collaborative signals and (2) GCL adopts a single aggregation paradigm, resulting in a lack of diversity in the feature representation of collaborative signals. To solve the above problems, a multi-view deep graph contrastive learning for collaborative filtering (MD-GCCF) has been proposed from two perspectives. First, a deep graph collaborative signal aggregation module is proposed to learn potential intention similarity representations for deep collaborative signal propagation within a few layers. Second, a novel multi-view contrastive learning module has been proposed, utilizing both local and global contrastive learning views from the collaborative signal aggregation module to enhance deep structures and semantic features in collaborative signals. MD-GCCF improves by 9.52%, 3.34%, and 2.49% compared to the rival models, respectively, in the Amazon book, Yelp2018, and Gowalla datasets.

Leader-Following Formation Control for Discrete-Time Fractional Stochastic Multi-Agent Systems by Event-Triggered Strategy

Fractional differential equations, which are non-local and can better describe memory and genetic properties, are widely used to describe various physical, chemical, and biological phenomena. Therefore, the multi-agent systems based on discrete-time fractional stochastic models are established. First, some followers are selected for pinning control. In order to save resources and energy, an event-triggered based control mechanism is proposed. Second, under this control mechanism, sufficient conditions on the interaction graph and the fractional derivative order such that formation control can be achieved are given. Additionally, influenced by noise, the multi-agent system completes formation control in the mean square. In addition to that, these results are equally applicable to the discrete-time fractional formation problem without noise. Finally, the example of numerical simulation is given to prove the correctness of the results.

Shape deformation, disintegration, and coalescence of suspension drops: Efficient simulation enabled by graph neural networks

Understanding the behaviors of suspension drops (particle swarms) as they settle in a viscous fluid holds significant importance across various applications. Due to hydrodynamic interactions (HIs), suspension drops would undergo a series of intricate behaviors, including shape deformation, disintegration, and coalescence. This work presents the hydrodynamic interaction graph neural network (HIGNN), developed in our prior work (Ma et al., 2022), as an efficient and accurate modeling framework for simulating the dynamics of suspension drops and investigating the various behaviors they exhibit. The HIGNN effectively incorporates the many-body nature of HIs, a feature lacking in most previous simulations that employ the Stokeslet assumption. In the meanwhile, the HIGNN achieves superior computational efficiency compared to traditional, high-fidelity numerical tools such as Stokesian dynamics and PDE solvers. Moreover, the HIGNN, once trained, is applicable to predicting suspension drops across a range of particle concentrations and under diverse forces (such as gravity and Coulombic interactions). Training the HIGNN only requires the data containing a small number of particles, leading to low training cost. Our results demonstrate that the HIGNN can effectively reproduce the various behaviors of suspension drops that were previously reported in literature. More specifically, a single, initially spherical drop slowly evolves into a torus-shaped drop, as particles escape from its rear and form a tail along the sedimenting direction. Subsequently, the torus breaks into secondary droplets, each undergoing a similar transition (deformation into a torus followed by disintegration), thereby leading to a repeating cascade. Further, we quantitatively analyze the correlation between the drop’s sedimentation velocity and volume fraction. We also propose new scaling laws for evaluating both the leakage rate of particles and the expansion rate of the horizontal radius of a suspension drop. For single suspension drops, we also systematically investigate how their dynamics is affected by their initial shapes and the formed tori’s aspect ratios, as well as with or without Coulombic interactions between particles. For a pair of suspension drops, we study the process of coalescence of two vertically aligned particles and examine the effect of introducing a horizontal offset on the subsequent breakup of the coalesced drop. All simulations were executed on a single GPU, with the computation of velocities for several thousand particles requiring less than five seconds per time step. This computational efficiency enables fast and resource-saving simulations of large suspension drops over extended time scales.