Multiple Types Of Information Research Articles

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Abstract The virtuality and openness of online social platforms make it a hotbed for the rapid propagation of various rumors. In order to block the outbreak of rumor, one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor. The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues. Firstly, in order to simulate the dissemination of multiple types of information, we propose a competitive linear threshold model with state transition (CLTST) to describe the spreading process of rumor and anti-rumor in the same network. Subsequently, we put forward a community-based rumor blocking algorithm (CRB) based on influence maximization theory in social networks. Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes, which includes community detection, selection of candidate anti-rumor seeds and generation of anti-rumor seed set. Under CLTST model, CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance. Experimental results show that the proposed model can better reflect the process of rumor propagation, and review the propagation mechanism of rumor and anti-rumor in online social networks. Moreover, the proposed CRB algorithm has better performance in weakening the rumor dissemination ability, which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread, sensitivity analysis, seeds distribution and running time.

A prediction model for rumor user propagation behavior based on sparse representation and transfer learning

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information

Compared with English named entity recognition (NER), Chinese NER faces significant challenges due to the flexible, non-standard word formation and vague word boundaries, which cause a lot of boundary ambiguity and reduce the accuracy of entity identification. To address this issue, we propose a boundary enhancement with multi-class information model (BEMCI). The model integrates multiple types of information into text embedding while enhancing the subsequent syntax-structure information. A syntactic information analysis module is designed to highlight important syntax information from three aspects, namely part-of-speech tags, syntactic constituents, and dependency relations, to analyze sentence structures. Meanwhile, an improved contextual attention mechanism, which combines contextual and syntactic information using a gate mechanism to control the weight fusion, is proposed to further enhance the model’s boundary determination. Multiple sets of experiments conducted on six general datasets show that BEMCI outperforms other baselines, achieving the best results in four of these six datasets.

Male Pagurus minutus hermit crabs use multiple types of information in decisions to give up male–male contests

Organisms use information to make adaptive decisions in various contexts, including aggression. Potentially weaker, but better-informed, contestants should give up earlier to reduce fighting costs by using information related to their own lower success such as their size relative to their opponent and past contest outcomes to make this choice. Here, we examined whether intruders of the hermit crab Pagurus minutus could use information about their (1) smaller size, (2) past contest defeats, (3) opponent’s past wins, or (4) relationship in the dominance hierarchy to their opponent when making a decision to give up during male–male contests for a female. In all trials, we randomly matched a smaller intruder with a larger opponent that was guarding a female. Our analyses suggest that P. minutus intruders can use all four types of information to decide whether to give up a contest without escalation or decrease its duration after escalation; it is the first species of Pagurus reported to do so, and the second reported to be able to distinguish familiar opponents from others in the context of male–male contests. These findings demonstrate the importance of cognitive abilities in minimizing costs when competing for vital resources.

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.

Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium. The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data. This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals. Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care. DERR1-10.2196/48521.

Competitive information propagation considering local-global prevalence on multi-layer interconnected networks

The popularity of online social networks (OSNs) promotes the co-propagation of multiple types of information. And there exist inevitably competitive interactions between these information, which will significantly affect the spreading trend of each information. Besides, the coupled topology of multi-layer interconnects exhibited in OSNs will also increase the research complexity of information propagation dynamics. To effectively address these challenges, we propose a novel competitive information propagation model on multi-layer interconnected networks, where the tendency of an individual to become a positive or negative spreader depends on the weighted consideration of local and global prevalence. Then the basic reproduction number is calculated via next-generation matrix method. And under the critical conditions of the basic reproduction number, the asymptotic stability of information-free and information-endemic equilibria is theoretically proven through Lyapunov stability theory. Besides, an optimal control problem involving two heterogeneous controls is formulated, aiming at achieving the best suppression performance of negative information with the minimum control cost. According to Cesari theorem and Pontryagin minimum principle, the existence and analytical formulation of optimal solutions are derived. Extensive numerical experiments are conducted to prove the correctness of our theoretical results, and evaluate the effectiveness of our proposed control strategies. This study can provide useful insights into the modeling and control of multiple information propagation considering multi-layer network topology and individual adaptive behavior.

Clinical Applications of Photon-counting CT: A Review of Pioneer Studies and a Glimpse into the Future.

CT systems equipped with photon-counting detectors (PCDs), referred to as photon-counting CT (PCCT), are beginning to change imaging in several subspecialties, such as cardiac, vascular, thoracic, and musculoskeletal radiology. Evidence has been building in the literature underpinning the many advantages of PCCT for different clinical applications. These benefits derive from the distinct features of PCDs, which are made of semiconductor materials capable of converting photons directly into electric signal. PCCT advancements include, among the most important, improved spatial resolution, noise reduction, and spectral properties. PCCT spatial resolution on the order of 0.25 mm allows for the improved visualization of small structures (eg, small vessels, arterial walls, distal bronchi, and bone trabeculations) and their pathologies, as well as the identification of previously undetectable anomalies. In addition, blooming artifacts from calcifications, stents, and other dense structures are reduced. The benefits of the spectral capabilities of PCCT are broad and include reducing radiation and contrast material dose for patients. In addition, multiple types of information can be extracted from a single data set (ie, multiparametric imaging), including quantitative data often regarded as surrogates of functional information (eg, lung perfusion). PCCT also allows for a novel type of CT imaging, K-edge imaging. This technique, combined with new contrast materials specifically designed for this modality, opens the door to new applications for imaging in the future.

Using heterogeneous sources of data and interpretability of prediction models to explain the characteristics of careless respondents in survey data

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey’s quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants’ personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models’ explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as “I would never take a bribe, even if it was a lot”, average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like “I would be tempted to use counterfeit money if I could get away with it” and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model.

Emergent information dynamics in many-body interconnected systems.

The information implicitly represented in the state of physical systems allows for their analysis using analytical techniques from statistical mechanics and information theory. This approach has been successfully applied to complex networks, including biophysical systems such as virus-host protein-protein interactions and whole-brain models in health and disease, drawing inspiration from quantum statistical physics. Here we propose a general mathematical framework for modeling information dynamics on complex networks, where the internal node states are vector valued, allowing each node to carry multiple types of information. This setup is relevant for various biophysical and sociotechnological models of complex systems, ranging from viral dynamics on networks to models of opinion dynamics and social contagion. Instead of focusing on node-node interactions, we shift our attention to the flow of information between network configurations. We uncover fundamental differences between widely used spin models on networks, such as voter and kinetic dynamics, which cannot be detected through classical node-based analysis. We illustrate the mathematical framework further through an exemplary application to epidemic spreading on a low-dimensional network. Our model provides an opportunity to adapt powerful analytical methods from quantum many-body systems to study the interplay between structure and dynamics in interconnected systems.

Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Breast cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. Machine learning algorithms have emerged as powerful tools for analyzing complex medical data and aiding in the diagnosis of breast cancer. This paper provides an overview of the application of machine learning algorithms in breast cancer diagnosis. The findings indicate that machine learning algorithms, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models, have been extensively explored for breast cancer diagnosis. These algorithms leverage the vast amounts of available data, including patient demographics, medical history, imaging data (mammography, ultrasound, MRI), and genetic profiles, to identify patterns and make predictions. One of the primary applications of machine learning algorithms in breast cancer diagnosis is the classification of tumors as malignant or benign. By training on labeled datasets, these algorithms can learn to differentiate between cancerous and non-cancerous cases, thus assisting in accurate tumor diagnosis. Additionally, machine learning algorithms can be used to predict the likelihood of cancer recurrence, which helps guide treatment decisions and post-treatment monitoring. Feature selection and extraction techniques also play a vital role in breast cancer diagnosis using machine learning algorithms. These techniques aim to identify the most relevant features or biomarkers associated with breast cancer, reducing the dimensionality of the data and enhancing the performance of the models. Feature selection algorithms, such as recursive feature elimination and correlation-based feature selection, contribute to the identification of critical indicators for accurate diagnosis. Furthermore, the integration of different data sources and modalities, such as combining clinical data with imaging data or genetic data, has shown promise in improving breast cancer diagnosis accuracy. By fusing multiple types of information, machine learning algorithms can leverage the complementary nature of these data sources to enhance diagnostic capabilities. Despite the advancements made, challenges remain in the field of breast cancer diagnosis using machine learning algorithms. Issues such as data quality, interpretability of models, and generalizability to diverse populations need to be addressed to ensure the reliable and equitable application of these algorithms in clinical practice.

A calculus of probability or belief? Neural underpinnings of social decision-making in a card game

For decades, the prefrontal cortex (PFC) has been the focus of social neuroscience research, specifically regarding its role in competitive social decision-making. However, the distinct contributions of PFC subregions when making strategic decisions involving multiple types of information (social, non-social, and mixed information) remain unclear. This study investigates decision-making strategies (pure probability calculation vs. mentalizing) and their neural representations using functional near-infrared spectroscopy (fNIRS) data collected during a two-person card game. We observed individual differences in information processing strategy, indicating that some participants relied more on probability than others. Overall, the use of pure probability decreased over time in favor of other types of information (e.g., mixed information), with this effect being more pronounced within-round trials than across rounds. In the brain, (1) the lateral PFC activates when decisions are driven by probability calculations; (2) the right lateral PFC responds to trial difficulty; and (3) the anterior medial PFC is engaged when decision-making involves mentalizing. Furthermore, neural synchrony, which reflects the real-time interplay between individuals' cognitive processes, did not consistently contribute to correct decisions and fluctuated throughout the experiment, suggesting a hierarchical mentalizing mechanism at work.

Tigers at a crossroads: Shedding light on the role of Bangladesh in the illegal trade of this iconic big cat

AbstractUnsustainable wildlife trade is a major threat to many species, but quantifying trade remains challenging, as seizure data provides an incomplete understanding. For this reason, integrating multiple types of information, including interviews with actors involved in trade, is crucial if we are to understand the problem better. Hence, in this study, we digitized Bangladesh Forest Department tiger seizure records to identify trade routes and interviewed 163 individuals involved in trafficking tigers through Bangladesh's air, sea and land ports, including poachers, smugglers, and traders. We identified six ports used to import tigers, 14 ports used for tiger export and three ports showing bi‐directional trade. Elite Bangladeshis were the most important consumer group, and tigers were sourced from populations in NE India, Myanmar and Bangladesh Sundarbans to supply domestic demand. Tiger products were exported to 14 countries, including seven G20 nations, with Bangladeshi expatriates as the consumer group in three countries (United Kingdom, Germany and Qatar). Rising economic development in Bangladesh over the last decade, combined with deep‐rooted cultural ties to tiger consumption, has led to a rise in domestic demand. Additionally, rapid growth in international transport links has increased smuggling and connected local traders with global markets, increasing the complexity of global trade. These findings suggest Bangladesh is poised to play a pivotal role in tiger conservation over the next decade, requiring strong national strategies to reduce trade opportunities, disrupt networks and weaken demand.

Nondestructive inspection and imaging of magnetic hydrogel using the alternating magnetic field infrared thermography

Magnetic hydrogel has great potential application in various fields, such as soft robots, drug delivery and biomedical engineering. We report a testing method, i.e., the infrared thermograph method based on alternating magnetic field excitation for the detection of magnetic hydrogel. We verify the feasibility of concentration evaluation of magnetic nanoparticles in magnetic hydrogels using this method and analyze the relationship between the surface temperature rise rate of the magnetic hydrogel and the nanoparticle concentration. Then, we use this method to inspect and image the distribution of magnetic particles. Besides, we validate that this method is effective for detecting bubble defects in magnetic hydrogel and imaging the three-dimensional distribution of the fibers added in the hydrogel, and metal fibers are mainly concerned. This work provides a clear insight into a nondestructive inspection and imaging method to magnetic hydrogel, and this method can be efficient to detect multiple types of information.

Template-based knowledge reuse method for generating high-speed railway virtual construction scenes

ABSTRACT Virtual construction has become an important approach to the high-quality development of high-speed railways, but existing methods have problems such as low efficiency in generating virtual construction scenes and the inability to reuse construction knowledge. To support the rapid visual representation of multiple types of construction processes and construction methods, a template-based knowledge reuse method is proposed. The method includes using a component-based modeling mode to build body structure models of a high-speed railway project and generate a 3D scene; decomposing the construction process and building a construction process knowledge base; establishing joint linkage models of construction machinery and forming a construction method knowledge template; and fusing multiple types of information according to a time sequence to visualize the construction process. Based on the template-based knowledge reuse method, a prototype system was developed, and virtual construction experiments were carried out. The results show that this method achieves the reuse of construction knowledge at different levels including construction machinery level, construction method level, and work site level. Compared with animation software for virtual construction, this method improves the production efficiency by 87%. Moreover, this method can provide a multilevel knowledge reuse scheme for diversified virtual construction.

Between health care and social services: Boundary objects and cross-sector collaboration

Health care systems throughout the United States are initiating collaborations with social services agencies. These cross-sector collaborations aim to address patients’ social needs—such as housing, food, income, and transportation—in health care settings. However, such collaborations can be challenging as health care and social service sectors are composed of distinct missions, institutions, professional roles, and modes of distributing resources. This paper examines how the “high-risk” patient with both medical and social needs is constructed as a shared object of intervention across sectors. Using the concept of boundary object, we illustrate how the high-risk patient category aggregates and represents multiple types of information—medical, social, service utilization, and cost—in ways that facilitate its use across sectors. The high-risk patient category works as a boundary object, in part, by the differing interpretations of “risk” available to collaborators. During 2019–2021, we conducted 75 semi-structured interviews and 31 field observations to investigate a relatively large-scale, cross-sector collaboration effort in California known as CommunityConnect. This program uses a predictive algorithm and big data sets to assign risk scores to the population and directs integrated health care and social services to patients identified as high risk. While the high-risk patient category worked well to foster collaboration in administrative and policy contexts, we find that it was less useful for patient-level interactions, where frontline case managers were often hesitant or unable to communicate information about the risk-based eligibility process. We suggest that the predominance of health care utilization (and its impacts on costs) in constructing the high-risk patient category may be medicalizing social services, with the potential to deepen inequities.

Some applications of Scanning Electron Microscopy to the study of reservoir rock

Scanning electron microscopy (SEM) is a powerful tool for visualising reservoir pore and grain framework systems which (SEM) provides qualitative information about pore geometry through direct observation of a rock or a pore cast of the rock. This aids in understanding reservoir productivity capabilities. The SEM is useful for locating and identifying minerals, particularly clay minerals - an aid when designing drilling and completion programs. SEM unlike conventional light microscopy, produces images by recording various signals resulting from interactions of an electron beam with the sample as it is scanned in a raster pattern across the sample surface. Combined with a backscatter detector (BSE), energy dispersal X-ray spectroscopy (EDS), and EDS - Mapping, SEM can yield multiple types of information about geological samples at the same time, such as superficial microstructure, BSE image, component analysis, and crystal structure features. In addition, new technic of SEM like Qemscan and FIB (Focused ion beam) can automatically minerals mapping and approach to 3-D imaging. In this paper, we use granite rock from a granitic basement reservoir, Bach Ho Oilfield as examples to discuss the geological application of SEM. The most important reservoir properties of granite rock in the oilfield are porosity and fractured pores which are abundant. The fractures have various sizes from mm to micron in width. Some large cracks have 1÷3 cm in lenght and 100÷600 µm in width. The small ones have 0.001÷0.2 mm in length and 10÷100 µm in width. Some large fractures were porefilled by crystals that have blade shape.

High-Confidence Sample Generation Technology and Application for Global Land-Cover Classification

Deep learning technology has become one of the most important technologies in remote sensing land classification applications. Its powerful sample-learning and information-mining abilities promote the continuous improvement of classification accuracy. A large volume of high-quality and representative sample data is the premise for the successful application of deep learning technology. Conventional methods of obtaining samples through manual delineation or surface surveys require a great deal of manpower and material resources. Therefore, the inability to obtain sufficient and widely representative high-quality samples is one of the key factors limiting the application of deep learning technology. In this study, the method of generating sample data obtains high-confidence classification results from a variety of existing high-quality classification products as deep learning samples, which are then used to support the application of deep learning technology for land-cover classification. When the three global land-cover classification products, FROM-GLC-2015, GLC_FCS30-2015, and GlobeLand30, have the same type of discrimination, the sample is considered a high-confidence sample. Based on this, a large volume of sample data widely distributed around the world was obtained. Using the extracted samples, a random forest classifier was trained using multiple types of information from the Landsat data, and land-cover classification was achieved. Application experiments were conducted in several typical regions, and the classification results were verified. The results showed that the classification accuracy of random forests under the support of samples generated using the sample extraction method proposed in this article was considerably higher than that of the three land-cover classification products.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study.

Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images. In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance. On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance. MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.

Drug response prediction using graph representation learning and Laplacian feature selection

Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying. In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-k predicted sensitive cell lines. Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference.