Common Representation Research Articles

JORA: Weakly Supervised User Identity Linkage via Jointly Learning to Represent and Align.

The user identity linkage that establishes correspondence between users across networks is a fundamental issue in various social network applications. Efforts have recently been devoted to introducing network embedding techniques that map the different network users into the common representation space, thereby inferring user correspondence based on the similarities of their representations. However, existing studies that separately train the network embedding and space alignment in two stages may lead to conflict between the objectives of the two stages. Besides, the similarities between unlabeled cross-network user pairs are difficult to define and largely impact the result. Moreover, many previous methods still need plenty of labeled aligned user pairs to ensure performance, which may not be available. To address the above problems, we propose to solve the weakly-supervised user identity linkage problem via JOintly learning to Represent and Align, i.e., the JORA model. The architecture of JORA adopts the inductive graph convolutional network (GCN) that learns representations for each network. The model is jointly optimized by the representation learning component and alignment learning component. The former one aims to preserve the similarities between intranetwork users. The latter one aligns the different spaces by a projection function and aims to preserve the similarities between cross-network users. A specific attention mechanism is proposed to learn self-adaptive similarities for unlabeled user pairs during alignment learning and it reduces the error propagation caused by predefined similarities. The joint optimization helps perceive network characteristics during alignment and reduces the number of labeled users required. Experiments conducted on real social networks show that the proposed model achieves significantly better performance than the state-of-the-art methods.

RDPGNet: A road extraction network with dual-view information perception based on GCN

Automatic road extraction from satellite images is a popular research hotspot topic in the field of remote sensing. However, the complexity of road features and structures hinders their precise extraction. Road features have different saliency due to the occlusion of other objects and diverse road types. Road structures are scattered across various locations and affected by geographical conditions, population distribution, and transportation demands. These issues render current methods based on convolutional neural networks prone to fragmented and incomplete road extraction results. To address these issues, a Road Extraction Network with Dual-View Information Perception Based on GCN (RDPGNet) is proposed, where the GCN-based dual-view perceptron (GDVP) uses the superior information interaction capability of the GCN to explore road information under a dual view. In the GDVP, we first design the road feature saliency graph reasoning module (RFSG) and road structure homogeneous space graph reasoning module (RSHS) based on the features and structures. The RFSG obtains the similarity of road feature information in different regions as a similarity matrix to participate in the graph reasoning process, ensuring that equal treatment is given to regions with varying saliency levels. The RSHS projects the road structure onto a corresponding homogeneous space while aggregating and interacting with information through graph convolution, thereby enhancing the network’s perception of roads in diverse locations. Second, because multi-view information usually shares a potential common representation, a multi-view information fusion and alignment strategy (MVFA) is designed to comprehensively model road information. Experimental results obtained from two public datasets indicate that RDPGNet outperforms other state-of-the-art networks.

Learning attentional templates for value-based decision-making

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed acrosstasks.

A semi-supervised cross-modal memory bank for cross-modal retrieval

The core of semi-supervised cross-modal retrieval tasks lies in leveraging limited supervised information to measure the similarity between cross-modal data. Current approaches assume an association between unlabelled data and pre-defined k-nearest neighbour data, relying on classifier performance for this selection. With diminishing labelled data, classifier performance weakens, resulting in erroneous associations among unlabelled instances. Moreover, the lack of interpretability in class probabilities of unlabelled data hinders classifier learning. Thus, this paper focuses on learning pseudo-labels for unlabelled data, providing pseudo-supervision to aid classifier learning. Specifically, a cross-modal memory bank is proposed, dynamically storing feature representations in a common space and class probability representations in a label space for each cross-modal data. Pseudo-labels are derived by computing feature representation similarity and adjusting class probabilities. During this process, imposing constraints on the classification loss between labelled data and contrastive losses between paired cross-modal data is a prerequisite for the successful learning of pseudo-labels. This procedure significantly contributes to enhancing the credibility of these pseudo-labels. Empirical findings demonstrate that using only 10% labelled data, compared to prevailing semi-supervised techniques, this method achieves improvements of 2.6%, 1.8%, and 4.9% in MAP@50 on the Wikipedia, NUS-WIDE, and MS-COCO datasets, respectively.

IF THIS THEN THAT Broken Linear Logic. Rethinking and Representing the Design Process

Since its origins, the discipline of Design has developed in close connection with technological progress, trying to adapt its processes, approaches and methodologies. What we are questioning is how much Design has actually been successful in converging towards a disciplinary model suitable to the most recent technological evolutions such as AI and data-driven approaches, and, first of all, how much the way of thinking of designers has changed in this direction. Through an evaluation of design approaches, methodologies and processes from an historical point of view, the aim of this paper is to surface the misalignment between contemporary design processes logic and the linearity of its common representations, due to recent technological advancements. In fact, the representation of a process affects the epistemology of the process itself. This contribution presents on-going research based on literature review and mapping of contemporary design processes structures and representations, in order to define some good attempts and practices useful for building more reliable representations of design processes able to deal with challenges in a highly-technologically advanced present.

Want a More Effective School? Better Start with the Culture

ABSTRACT In this article, the relationships between the nature of the professional leadership culture and student learning are examined according to the professionals’ perceptions. The qualitative case study examined the perceptions of school-level professionals in one elementary school with a reputation for strong results in student learning, regarding the nature of the professional leadership culture and its relationship to student learning. Professional leadership culture is defined by this author as shared assumptions, practices, beliefs and values concerning leadership activities that are understood and practiced among school professionals. E. H. Schein’s (2010) organizational culture and leadership model provided the conceptual framework for the study. The three most common representations of the professional leadership culture included pervasive collaboration among professionals, a culture of trust, and a supportive environment. This study prompts some reflection for theory-building on the connection between the concept of leadership and its role in improving school culture. Policy implications of the study are discussed as they relate to human resources issues and their connection to culture and leadership, priorities regarding personnel supervision and evaluation, standards for a respectful workplace, the institutionalization of trust, open communication, supportive relationships, collaboration, and the anticipation and management of workplace conflict.

Is the “Common Home” Metaphor Adequate and Useful for an “Integral Ecology” Theology in Modern Times?

This essay argues that the “common home” metaphor, when applied to planet Earth, falls short in its ability to provide an accurate analogy with the complexity and diversity of the planet itself since it has a limited epistemological, heuristic, and hermeneutical horizon; it is an analogy that proves inadequate in expressing common human representations of home and the two principles that should inspire an Ecotheology: the ontological value of creatures (Gaudium et Spes) and the recognition of the intrinsic relationship between all beings (Laudato Si’). In order to methodologically support this enquiry, a reflexive analysis and a metadisciplinary discourse are used through Emmanuel Levinas, Hannah Arendt, and the concept of integral ecology, proposed in Pope Francis’s encyclical letter Laudato Si’. On care for our common home, 21 May 2015. The performativity of the “common home” metaphor is evaluated to review its use in Ecotheology. The conclusion reached is that the category of “common life” might be more appropriate than “common home” to characterize how humans inhabit the world for an Ecotheology, and to represent planetary and cosmic communion and interdependence.

Asymmetric Supervised Fusion-Oriented Hashing for Cross-Modal Retrieval.

Hashing technologies have been widely applied for large-scale multimodal retrieval tasks owing to their excellent performance in search and storage tasks. Although some effective hashing methods have been proposed, it is still difficult to handle the intrinsic linkages that exist among different heterogeneous modalities. Moreover, optimizing the discrete constraint problem through a relaxation-based strategy results in a large quantization error and leads to a suboptimal solution. In this article, we present a novel asymmetric supervised fusion-oriented hashing method, named (ASFOH), which investigates three novel schemes to remedy the above issues. Specifically, we first explicitly formulate the problem as matrix decomposition into a common latent representation and a transformation matrix, combined with an adaptive weight scheme and nuclear norm minimization to ensure the information completeness of multimodal data. Then, we associate the common latent representation with the semantic label matrix, thereby increasing the discriminative capability of the model by constructing an asymmetric hash learning framework, thus, making the generated hash codes more compact. Finally, an efficient discrete optimization iterative algorithm based on nuclear norm minimization is proposed to decompose the nonconvex multivariate optimization problem into several subproblems with analytical solutions. Comprehensive experiments on the MIRFlirck, NUS-WIDE, and IARP-TC12 datasets testify that ASFOH outperforms the compared state-of-the-art approaches.

Robust detection of clinically relevant features in single-cell RNA profiles of patient-matched fresh and formalin-fixed paraffin-embedded (FFPE) lung cancer tissue

PurposeSingle-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. So far, single-cell studies have been constrained by the requirement for prospectively collected fresh or cryopreserved tissue. This limitation might be overcome by recent technical developments enabling single-cell analysis of FFPE tissue.MethodsWe benchmark single-cell profiles from patient-matched fresh, cryopreserved and archival FFPE cancer tissue.ResultsWe find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathway, hallmark, and clinically useful signatures, although expression of single genes varied due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting.ConclusionOur analysis highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and increases the applicability in translational research.

Knowledge enhancement for speech emotion recognition via multi-level acoustic feature

Speech emotion recognition (SER) has become an increasingly attractive machine learning task for domain applications. It aims to improve the discriminative capacity of speech emotion utilising a certain type of features (e.g. MFCC, Spectrograms, Wav2vec2) or multi-type combination features. However, the potential of acoustic-related deep features is frequently overlooked in existing approaches that rely solely on a single type of feature or employ a basic combination of multiple feature types. To address this challenge, a multi-level acoustic feature cross-fusion approach is proposed, aiming to compensate for missing information between various features. It helps to enhance the SER performance by integrating different types of knowledge through the cross-fusion mechanism. Moreover, multi-task learning is utilised to share useful information through gender recognition, which can also obtain multiple common representations in a fine-grained space. Experimental results show that the fusion approach can capture the inner connections between multilevel acoustic features to refine the knowledge. The SOTA results were obtained under the same experimental conditions.

CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow.

To present an open-source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase-contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk-motion phase sensitivity took approximately 10 s per diffusion-weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open-source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

Incomplete Cross-Modal Retrieval with Deep Correlation Transfer

Most cross-modal retrieval methods assume the multi-modal training data is complete and has a one-to-one correspondence. However, in the real world, multi-modal data generally suffers from missing modality information due to the uncertainty of data collection and storage processes, which limits the practical application of existing cross-modal retrieval methods. Although some solutions have been proposed to generate the missing modality data using a single pseudo sample, this may lead to incomplete semantic restoration and sub-optimal retrieval results due to the limited semantic information it provides. To address this challenge, this article proposes an Incomplete Cross-Modal Retrieval with Deep Correlation Transfer (ICMR-DCT) method that can robustly model incomplete multi-modal data and dynamically capture the adjacency semantic correlation for cross-modal retrieval. Specifically, we construct intra-modal graph attention-based auto-encoder to learn modality-invariant representations by performing semantic reconstruction through intra-modality adjacency correlation mining. Then, we design dual cross-modal alignment constraints to project multi-modal representations into a common semantic space, thus bridging the heterogeneous modality gap and enhancing the discriminability of the common representation. We further introduce semantic preservation to enhance adjacency semantic information and achieve cross-modal semantic correlation. Moreover, we propose a nearest-neighbor weighting integration strategy with cross-modal correlation transfer to generate the missing modality data according to inter-modality mapping relations and adjacency correlations between each sample and its neighbors, which improves the robustness of our method against incomplete multi-modal training data. Extensive experiments on three widely tested benchmark datasets demonstrate the superior performance of our method in cross-modal retrieval tasks under both complete and incomplete retrieval scenarios. Our used datasets and source codes are available at https://github.com/shidan0122/DCT.git .

Cortical quantity representations of visual numerosity and timing overlap increasingly into superior cortices but remain distinct

Many sensory brain areas are organized as topographic maps where neural response preferences change gradually across the cortical surface. Within association cortices, 7-Tesla fMRI and neural model-based analyses have also revealed many topographic maps for quantities like numerosity and event timing, often in similar locations. Numerical and temporal quantity estimations also show behavioral similarities and even interactions. For example, the duration of high-numerosity displays is perceived as longer than that of low-numerosity displays. Such interactions are often ascribed to a generalized magnitude system with shared neural responses across quantities. Anterior quantity responses are more closely linked to behavior. Here, we investigate whether common quantity representations hierarchically emerge by asking whether numerosity and timing maps become increasingly closely related in their overlap, response preferences, and topography. While the earliest quantity maps do not overlap, more superior maps overlap increasingly. In these overlapping areas, some intraparietal maps have consistently correlated numerosity and timing preferences, and some maps have consistent angles between the topographic progressions of numerosity and timing preferences. However, neither of these relationships increases hierarchically like the amount of overlap does. Therefore, responses to different quantities are initially derived separately, then progressively brought together, without generally becoming a common representation. Bringing together distinct responses to different quantities may underlie behavioral interactions and allow shared access to comparison and action planning systems.

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.

Turning the Light Switch on Binding: Prefrontal Activity for Binding and Retrieval in Action Control.

According to action control theories, responding to a stimulus leads to the binding of response and stimulus features into a common representation, that is, an event file. Repeating any component of an event file retrieves all previously bound information, leading to performance costs for partial repetitions measured in so-called binding effects. Although otherwise robust and stable, binding effects are typically completely absent in "localization tasks," in which participants localize targets with spatially compatible responses. Yet, it is possible to observe binding effects in such when location features have to be translated into response features. We hypothesized that this modulation of binding effects is reflected in task involvement of the dorsolateral pFC (DLPFC). Participants localized targets with either direct (i.e., spatially compatible key) or translated (i.e., diagonally opposite to the spatially compatible key) responses. We measured DLPFC activity with functional near-infrared spectroscopy. On the behavioral level, we observed binding effects in the translated response condition, but not in the direct response condition. Importantly, prefrontal activity was also higher in the translated mapping condition. In addition, we found some evidence for the strength of the difference in binding effects in behavioral data being correlated with the corresponding effects in prefrontal activity. This suggests that activity in the DLPFC reflects the amount of executive control needed for translating location features into responses. More generally, binding effects seem to emerge only when the task at hand involves DLPFC recruitment.

Dynamic 3D Point Cloud Sequences as 2D Videos.

Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing complex spatio-temporal point neighbor grouping and feature aggregation schemes, often resulting in methods lacking effectiveness, efficiency, and expressive power. In this paper, we propose a novel generic representation called Structured Point Cloud Videos (SPCVs). Intuitively, by leveraging the fact that 3D geometric shapes are essentially 2D manifolds, SPCV re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points. The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences. To achieve such re-organization, we design a self-supervised learning pipeline that is geometrically regularized and driven by self-reconstructive and deformation field learning objectives. Additionally, we construct SPCV-based frameworks for both low-level and high-level 3D point cloud sequence processing and analysis tasks, including action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the versatility and superiority of the proposed SPCV, which has the potential to offer new possibilities for deep learning on unstructured 3D point cloud sequences. Code will be released at https://github.com/ZENGYIMING-EAMON/SPCV.

The importance of observing the master's hand: Action Observation Training promotes the acquisition of new musical skills.

Via mirror mechanism, motor training approaches based on the alternation of action observation and execution (i.e., Action Observation Training-AOT) promote the acquisition of motor abilities. Previous studies showed that both visual and auditory stimuli may elicit a common motor representation of music-related gestures; however, the potentialities of AOT for the acquisition of musical skills are still underexplored. Twenty-one music-naïve participants underwent two blocks of training: AOT and Key-light Observation Training (KOT). AOT consisted of the observation of a melodic sequence played on a keyboard with the right hand by an expert model, followed by participant's imitation. Observation and execution were repeated six consecutive times (T1-T6). KOT followed the same procedure, except for the visual content of the stimulus, depicting the sequential highlighting of the piano keys corresponding to the melody. The rate of correct notes (C), the trainee-model similarity of key-pressure strength (S), and the trainee-model consistency of note duration (R) were collected across T1-T6. Both AOT and KOT improved musical performance. Noteworthy, AOT showed a higher learning magnitude relative to KOT in terms of C and S. Action Observation Training promotes the acquisition of key elements of melodic sequences, encompassing not only the accurate sequencing of notes but also their expressive characteristics, such as key-pressure dynamics. The convergence of listening and observation of actions onto a shared motor representation not only explains several pedagogical approaches applied in all musical cultures worldwide, but also enhances the potential efficacy of current procedures for music training.

Efficient Communication via Self-Supervised Information Aggregation for Online and Offline Multiagent Reinforcement Learning.

Utilizing messages from teammates can improve coordination in cooperative multiagent reinforcement learning (MARL). Previous works typically combine raw messages of teammates with local information as inputs for policy. However, neglecting message aggregation poses significant inefficiency for policy learning. Motivated by recent advances in representation learning, we argue that efficient message aggregation is essential for good coordination in cooperative MARL. In this article, we propose Multiagent communication via Self-supervised Information Aggregation (MASIA), where agents can aggregate the received messages into compact representations with high relevance to augment the local policy. Specifically, we design a permutation-invariant message encoder to generate common information-aggregated representation from messages and optimize it via reconstructing and shooting future information in a self-supervised manner. Hence, each agent would utilize the most relevant parts of the aggregated representation for decision-making by a novel message extraction mechanism. Furthermore, considering the potential of offline learning for real-world applications, we build offline benchmarks for multiagent communication, which is the first as we know. Empirical results demonstrate the superiority of our method in both online and offline settings. We also release the built offline benchmarks in this article as a testbed for communication ability validation to facilitate further future research in this direction.

Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering.

The success of multiview raw data mining relies on the integrity of attributes. However, each view faces various noises and collection failures, which leads to a condition that attributes are only partially available. To make matters worse, the attributes in multiview raw data are composed of multiple forms, which makes it more difficult to explore the structure of the data especially in multiview clustering task. Due to the missing data in some views, the clustering task on incomplete multiview data confronts the following challenges, namely: 1) mining the topology of missing data in multiview is an urgent problem to be solved; 2) most approaches do not calibrate the complemented representations with common information of multiple views; and 3) we discover that the cluster distributions obtained from incomplete views have a cluster distribution unaligned problem (CDUP) in the latent space. To solve the above issues, we propose a deep clustering framework based on subgraph propagation and contrastive calibration (SPCC) for incomplete multiview raw data. First, the global structural graph is reconstructed by propagating the subgraphs generated by the complete data of each view. Then, the missing views are completed and calibrated under the guidance of the global structural graph and contrast learning between views. In the latent space, we assume that different views have a common cluster representation in the same dimension. However, in the unsupervised condition, the fact that the cluster distributions of different views do not correspond affects the information completion process to use information from other views. Finally, the complemented cluster distributions for different views are aligned by contrastive learning (CL), thus solving the CDUP in the latent space. Our method achieves advanced performance on six benchmarks, which validates the effectiveness and superiority of our SPCC.

CCGIB: A Cross-Channel Graph Information Bottleneck Principle.

The empirical studies of most existing graph neural networks (GNNs) broadly take the original node feature and adjacency relationship as single-channel input, ignoring the rich information of multiple graph channels. To circumvent this issue, the multichannel graph analysis framework has been developed to fuse graph information across channels. How to model and integrate shared (i.e., consistency) and channel-specific (i.e., complementarity) information is a key issue in multichannel graph analysis. In this article, we propose a cross-channel graph information bottleneck (CCGIB) principle to maximize the agreement for common representations and the disagreement for channel-specific representations. Under this principle, we formulate the consistency and complementarity information bottleneck (IB) objectives. To enable optimization, a viable approach involves deriving variational lower bound and variational upper bound (VarUB) of mutual information terms, subsequently focusing on optimizing these variational bounds to find the approximate solutions. However, obtaining the lower bounds of cross-channel mutual information objectives proves challenging through direct utilization of variational approximation, primarily due to the independence of the distributions. To address this challenge, we leverage the inherent property of joint distributions and subsequently derive variational bounds to effectively optimize these information objectives. Extensive experiments on graph benchmark datasets demonstrate the superior effectiveness of the proposed method.