Structural Information Research Articles

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification.

Recently, Graph Neural Networks (GNNs) have gained widespread application in automatic brain network classification tasks, owing to their ability to directly capture crucial information in non-Euclidean structures. However, two primary challenges persist in this domain. First, within the realm of clinical neuro-medicine, signals from cerebral regions are inevitably contaminated with noise stemming from physiological or external factors. The construction of brain networks heavily relies on set thresholds and feature information within brain regions, making it susceptible to the incorporation of such noises into the brain topology. Additionally, the static nature of the artificially constructed brain network's adjacent structure restricts real-time changes in brain topology. Second, mainstream GNN-based approaches tend to focus solely on capturing information interactions of nearest neighbor nodes, overlooking high-order topology features. In response to these challenges, we propose an adaptive unsupervised Spatial-Temporal Dynamic Hypergraph Information Bottleneck (ST-DHIB) framework for dynamically optimizing brain networks. Specifically, adopting an information theory perspective, Graph Information Bottleneck (GIB) is employed for purifying graph structure, and dynamically updating the processed input brain signals. From a graph theory standpoint, we utilize the designed Hypergraph Neural Network (HGNN) and Bi-LSTM to capture higher-order spatial-temporal context associations among brain channels. Comprehensive patient-specific and cross-patient experiments have been conducted on two available datasets. The results demonstrate the advancement and generalization of the proposed framework.

SimClone: Detecting Tabular Data Clones using Value Similarity

Data clones are defined as multiple copies of the same data among datasets. Presence of data clones between datasets can cause issues such as difficulties in managing data assets and data license violations when using datasets with clones to build AI software. However, detecting data clones is not trivial. Majority of the prior studies in this area rely on structural information to detect data clones (e.g., font size, column header). However, tabular datasets used to build AI software are typically stored without any structural information. In this paper, we propose a novel method called SimClone for data clone detection in tabular datasets without relying on structural information. SimClone method utilizes value similarities for data clone detection. We also propose a visualization approach as a part of our SimClone method to help locate the exact position of the cloned data between a dataset pair. Our results show that our SimClone outperforms the current state-of-the-art method by at least 20% in terms of both F1-score and AUC. In addition, SimClone’s visualization component helps identify the exact location of the data clone in a dataset with a Precision@10 value of 0.80 in the top 20 true positive predictions.

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefactremoval. Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for trainingdata. We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging ofpatients. The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truthimages. Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinicalradiology.

The role of Nuclear Magnetic Resonance (NMR) spectroscopy in cattle metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool that has significantly advanced the understanding of cattle metabolism. Nuclear Magnetic Resonance (NMR) spectroscopy plays a pivotal role in the study of cattle metabolism, offering distinct advantages over other spectrometric methods. NMR spectroscopy is a powerful analytical tool that provides detailed molecular insights by exploiting the magnetic properties of atomic nuclei. Unlike mass spectrometry and infrared spectroscopy, NMR does not require extensive sample preparation or destruction, preserving the integrity of biological samples. This non-invasive nature is particularly beneficial for longitudinal studies in cattle, where metabolic changes over time are of interest. One of the key strengths of NMR spectroscopy is its ability to simultaneously detect and quantify a broad range of metabolites in complex biological matrices, such as blood, urine, and tissue extracts. This comprehensive metabolic profiling is crucial for understanding the biochemical pathways and physiological states in cattle. NMR's high reproducibility and quantitative accuracy further enhance its suitability for metabolic studies, enabling precise monitoring of metabolic fluctuations in response to dietary changes, environmental stressors, or disease conditions. NMR spectroscopy also offers unique advantages in elucidating structural information about metabolites. Through multidimensional NMR techniques, researchers can determine the molecular structure and conformation of metabolites, providing deeper insights into metabolic functions and interactions. This structural elucidation is often challenging with other spectrometric methods, which may lack the resolution or require derivatization of samples. Moreover, NMR spectroscopy's non-destructive nature allows for the analysis of living tissues and in vivo studies, facilitating real-time monitoring of metabolic processes. This capability is instrumental in studying dynamic metabolic responses and adaptations in cattle under different physiological states. Additionally, the development of advanced NMR techniques, such as high-resolution magic angle spinning (HR-MAS) and hyperpolarization, has further expanded the scope of NMR applications in metabolic research. NMR spectroscopy stands out as a superior method for studying cattle metabolism due to its non-destructive approach, comprehensive metabolic profiling, structural elucidation capabilities, and potential for in vivo analysis. These advantages make NMR an indispensable tool in advancing our understanding of cattle metabolism and improving livestock health and productivity. Keywords: Nuclear Magnetic Resonance (NMR), Cattles, Metabolomics.

Reliable Detection of Chemical Warfare Agents Using High Kinetic Energy Ion Mobility Spectrometry.

High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) ionize and separate ions at reduced pressures of 10-40 mbar and over a wide range of reduced electric field strengths E/N of up to 120 Td. Their reduced operating pressure is distinct from that of conventional drift tube ion mobility spectrometers that operate at ambient pressure for trace compound detection. High E/N can lead to a field-induced fragmentation pattern that provides more specific structural information about the analytes. In addition, operation at high E/N values adds the field dependence of ion mobility as an additional separation dimension to low-field ion mobility, making interfering compounds less likely to cause a false positive alarm. In this work, we study the chemical warfare agents tabun (GA), sarin (GB), soman (GD), cyclosarin (GF) and sulfur mustard (HD) in a HiKE-IMS at variable E/N in both the reaction and the drift region. The results show that varying E/N can lead to specific fragmentation patterns at high E/N values combined with molecular signals at low E/N. Compared to the operation at a single E/N value in the drift region, the variation of E/N in the drift region also provides the analyte-specific field dependence of ion mobility as additional information. The accumulated data establish a unique fingerprint for each analyte that allows for reliable detection of chemical warfare agents even in the presence of interfering compounds with similar low-field ion mobilities, thus reducing false positives.

A Hierarchical Low-Rank Denoising Model for Remote Sensing Images Based on Deep Unfolding

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results.

Probing the quantum nature of gravity using a Bose-Einstein condensate

The effect of noise induced by gravitons has been investigated using a Bose-Einstein condensate. The general complex scalar field theory with a quadratic self-interaction term has been considered in the presence of a gravitational wave. The gravitational wave perturbation is then considered as a sum of discrete Fourier modes in the momentum space. Varying the action and making use of the principle of least action, one obtains two equations of motion corresponding to the gravitational perturbation and the time-dependent part of the pseudo-Goldstone boson. Coming to an operatorial representation and quantizing the phase space variables via appropriately introduced canonical commutation relations between the canonically conjugate variables corresponding to the graviton and bosonic part of the total system, one obtains a proper quantum gravity setup. Then we obtain the Bogoliubov coefficients from the solution of the time-dependent part of the pseudo-Goldstone boson and construct the covariance metric for the bosons initially being in a squeezed state. The entries of the covariance matrix now involves a stochastic contribution which results in an operatorial stochastic structure of the quantum Fisher information. Using the stochastic average of the Fisher information, we obtain a lower bound on the amplitude parameter of the gravitational wave. As the entire calculation is done at zero temperature, the bosonic system, by construction, will behave as a Bose-Einstein condensate. For a Bose-Einstein condensate with a single mode, we observe that the lower bound of the expectation value of the square of the uncertainty in the amplitude measurement does not become infinite when the total observational term approaches zero. It always has a finite value if the gravitons are initially in a squeezed state with high enough squeezing. In order to sum over all possible momentum modes, we next consider a noise term with a suitable Gaussian weight factor which decays over time. We then obtain the lower bound on the final expectation value of the square of the variance in the amplitude parameter. Because of the noise induced by the graviton, there is a minimum value of the measurement time below which it is impossible to detect any gravitational wave using a Bose-Einstein condensate. Finally, we consider interaction between the phonon modes of the Bose-Einstein condensate which results in a decoherence. We observe that the decoherence effect becomes significant for gravitons with minimal squeezing. Published by the American Physical Society 2024

Structure-guided drug design targeting Abl kinase: how structure and regulation can assist in designing new drugs.

The human protein Abelson kinase (Abl), a tyrosine kinase, plays a pivotal role in developing chronic myeloid leukemia (CML). Abl's involvement in various signaling pathways underscores its significance in regulating fundamental biological processes, including DNA damage responses, actin polymerization, and chromatin structural changes. The discovery of the Bcr-Abl oncoprotein, resulting from a chromosomal translocation in CML patients, revolutionized the understanding and treatment of the disease. The introduction of targeted therapies, starting with interferon-alpha and culminating in the development of tyrosine kinase inhibitors (TKIs) like imatinib, significantly improved patient outcomes. However, challenges such as drug resistance and side effects persist, indicating the necessity of research into novel therapeutic strategies. This review describes advancements in Abl kinase inhibitor development, emphasizing rational compound design from structural and regulatory information. Strategies, including bivalent inhibitors, PROTACs, and compounds targeting regulatory domains, promise to overcome resistance and minimize side effects. Additionally, leveraging the intricate structure and interactions of Bcr-Abl may provide insights into developing inhibitors for other kinases. Overall, this review highlights the importance of continued research into Abl kinase inhibition and its broader implications for therapeutic interventions targeting kinase-driven diseases. It provides valuable insights and strategies that may guide the development of next-generation therapies.

MGPPI: multiscale graph neural networks for explainable protein–protein interaction prediction

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

Entity-Alignment Interaction Model Based on Chinese RoBERTa

Entity alignment aims to match entities with the same semantics from different knowledge graphs. Most existing studies use neural networks to combine graph-structure information and additional entity information (such as names, descriptions, images, and attributes) to achieve entity alignment. However, due to the heterogeneity of knowledge graphs, aligned entities often do not have the same neighbors, which makes it difficult to utilize the structural information from knowledge graphs and results in a decrease in alignment accuracy. Therefore, in this paper, we propose an interaction model that exploits only the additional information on entities. Our model utilizes names, attributes, and neighbors of entities for interaction and introduces attention interaction to extract features to further evaluate the matching scores between entities. Our model is applicable to Chinese datasets, and experimental results show that it has achieved good results on the Chinese medical datasets denoted MED-BBK-9K.

In Situ XRD Measurement for High-Pressure Iron in Laser-Driven Off-Hugoniot State

The investigation of iron under high pressure and temperatures is crucial to understand the Earth’s core structure and composition and the generation of magnetic fields. Here, we present new in situ XRD measurements for iron in an off-Hugoniot state by laser-driven ramp compression at pressure of 200–238 GPa. The lattice parameters for the hexagonal (hcp)-Fe phase and the c/a ratios were obtained to compare them with previous static and dynamical data, which provides the direct confirmation of such parameters via the different compression paths and strain rates. This work indicates that laser ramp compression can be utilized to provide crystal structure information and direct key information on the crystal structure of Fe at the ultrahigh pressure–temperature conditions relevant for planetology.

Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

Formation of the Idea of Cloud Technology and Its Legal Features in the Kyrgyz Republic

The article discusses the ideas of forming cloud technologies in the general information structure of the Kyrgyz Republic, features, disadvantages, as well as ways to further improve cloud systems. The experience of building cloud technologies at various stages of information development of the state in the form of creating strategies, programs and concepts has been studied. Proposals for the development of cloud technologies aimed at improving individual management methods in the system of state information complexes have been identified.

Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares–discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

Multi-Source Information Graph Embedding with Ensemble Learning for Link Prediction

Link prediction is a key technique for connecting entities and relationships in a graph reasoning field. It leverages known information about the graph structure data to predict missing factual information. Previous studies have either focused on the semantic representation of a single triplet or on the graph structure data built on triples. The former ignores the association between different triples, and the latter ignores the true meaning of the node itself. Furthermore, common graph-structured datasets inherently face challenges, such as missing information and incompleteness. In light of this challenge, we present a novel model called Multi-source Information Graph Embedding with Ensemble Learning for Link Prediction (EMGE), which can effectively improve the reasoning of link prediction. Ensemble learning is systematically applied throughout the model training process. At the data level, this approach enhances entity embeddings by integrating structured graph information and unstructured textual data as multi-source information inputs. The fusion of these inputs is effectively addressed by introducing an attention mechanism. During the training phase, the principle of ensemble learning is employed to extract semantic features from multiple neural network models, facilitating the interaction of enriched information. To ensure effective model learning, a novel loss function based on contrastive learning is devised, effectively minimizing the discrepancy between predicted values and the ground truth. Moreover, to enhance the semantic representation of graph nodes in link prediction, two rules are introduced during the aggregation of graph structure information. These rules incorporate the concept of spreading activation, enabling a more comprehensive understanding of the relationships between nodes and edges in the graph. During the testing phase, the EMGE model is validated on three datasets, including WN18RR, FB15k-237, and a private Chinese financial dataset. The experimental results demonstrate a reduction in the mean rank (MR) by 0.2 times, an improvement in the mean reciprocal rank (MRR) by 5.9%, and an increase in the Hit@1 by 12.9% compared to the baseline model.

Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery

Noises widely exist in the actual working environment of rotating machinery, which makes it difficult to extract high-quality fault features. Recently, the superiority of graph neural network (GNN) in the aspect of fault feature representation has been confirmed. By converting one-dimensional signals into graph structural data, hidden structural information inside the signals can be revealed. In this study, a novel graph-level fault diagnosis method based on spatial–temporal graph feature learning is developed. It mainly contains the following steps: First, Short-Time Fourier Transform (STFT) is used to construct time–frequency energy heatmaps, which can reflect the distribution of frequencies over time windows and the temporal variation characteristics. Second, a time–frequency similarity assessment method based on dynamic frequency warping is presented. On this basis, a time–frequency similarity graph (TFSG) is constructed to describe the frequency relationship over different time windows, which can bring extra structural information for the domain of time–frequency to support the subsequent classification task. Then, with the input of TFSG, a graph-level fault diagnosis model based on an improved GraphSAGE network (IGN) is developed. Finally, the fault labels for all TFSGs are recognized with a SoftMax classifier. Two experimental cases based on axial flow pumps and a public gearbox dataset verified the effectiveness of the proposed fault diagnosis method.

Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

Ubiquitous purine sensor modulates diverse signal transduction pathways in bacteria

Purines and their derivatives control intracellular energy homeostasis and nucleotide synthesis, and act as signaling molecules. Here, we combine structural and sequence information to define a purine-binding motif that is present in sensor domains of thousands of bacterial receptors that modulate motility, gene expression, metabolism, and second-messenger turnover. Microcalorimetric titrations of selected sensor domains validate their ability to specifically bind purine derivatives, and evolutionary analyses indicate that purine sensors share a common ancestor with amino-acid receptors. Furthermore, we provide experimental evidence of physiological relevance of purine sensing in a second-messenger signaling system that modulates c-di-GMP levels.

Earnings management in the family business groups of Pakistan: the role of corporate governance

Purpose This paper aims to examine the role of corporate governance (CG) in the earnings management (EM) of affiliated companies in family business groups (FBGs) listed on the Pakistan Stock Exchange (PSX), using principal–principal agency theory. Design/methodology/approach The sample of 327 nonfinancial firms of the PSX, consisting of 187 group-affiliated firms and 140 nonaffiliated firms has been used in this study for the period of 2010 to 2019. The study uses different regression models for analysis, with robustness tests of various alternative measures of EM and FBG affiliation. In addition, endogeneity is controlled with the propensity score matching method. Findings The findings show that EM is less prevalent in affiliated firms compared to nonaffiliated companies. The results show a negative and significant relationship between FBGs affiliated firms and EM. Moreover, the results also show a positive relationship between EM and the interaction term of the CG index and group affiliation. It refers to the fact that effective governance cannot reduce EM in affiliated companies of FBGs as well as in the nonfinancial companies of the PSX. In addition, the quality of CG is higher in affiliated companies compared to its counterpart in nonaffiliated firms. The findings support the principal–principal agency theory that CG cannot mitigate the expropriating behavior of controlling shareholders against minority shareholders by reducing EM in emerging markets due to the ownership concentration phenomenon. Research limitations/implications This research study has implications for small investors, government agencies and regulators. The findings of the study show that CG code should make it mandatory for companies to reveal information about their complex ownership structure and ownership information about affiliated companies and directors. Furthermore, it is suggested to revisit the code of CG in the Pakistani context of principal–principal conflict instead of the agent–principal explanation of agency theory based on Anglo–Saxon countries. Originality/value This research study has contributed to the CG and FBG literature in relation to EM in idiosyncratic settings of Pakistan. One of the prime contributions of the paper is the development of a comprehensive CG index. This research study used detailed, manually collected novel data on affiliated firms of FBGs in Pakistan.