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rady3's Blog

website·Indexed 29 days ago

https://rady3.github.io/test3/

A technical blog featuring articles on frontend development, NLP, and algorithms. Topics include Vue 3 component communication, binary tree traversal, route authentication, and deep learning models such as GPT-2, T5, and Deep Graph Infomax.

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What's inside rady3

  1. Overview of Simple Contrastive Graph Clustering (SCGC)

    Simple Contrastive Graph Clustering (SCGC) is a deep graph clustering model designed to address the inefficiencies of complex data augmentation and time-consuming graph convolutions. It improves clustering capability through three main components: a Siamese network architecture using MLPs, a simplified data augmentation strategy using Gaussian noise, and a neighbor-oriented contrastive objective function that maintains cross-view structural consistency.

  2. Overview of AGE (Adaptive Graph Encoder)

    AGE is an attributed graph embedding framework designed to overcome limitations in GCNs, such as the entanglement of filters and weight matrices and suboptimal low-pass characteristics. It consists of two modules: a Laplacian smoothing filter to mitigate high-frequency noise in node features, and an adaptive encoder that iteratively enhances filtered features for better node embeddings.

  3. Overview of Deep Graph Infomax (DGI) for Unsupervised Graph Embedding

    Deep Graph Infomax (DGI), introduced at ICLR 2019, is an unsupervised graph embedding algorithm based on mutual information maximization. Unlike traditional random-walk based methods that overemphasize proximity information and are highly sensitive to hyperparameters, DGI aims to maximize the mutual information between local node representations (patches) and a global graph summary representation.

  4. Overview of Multi-Scale Contrastive Siamese Networks (MSCSN)

    Multi-Scale Contrastive Siamese Networks (MSCSN) is a self-supervised framework for graph representation learning designed to learn node embeddings without heavy reliance on labeled data. It utilizes a momentum-driven siamese architecture consisting of an 'online network' and a 'target network'. The target network acts as a stable 'mean teacher' to encode historical observations, guiding the online network to learn richer representations and preventing representation collapse without needing excessive negative samples. The framework optimizes two primary objectives: cross-network contrastive learning and cross-view contrastive learning.

  5. Overview of GRACE (Deep Graph Contrastive Representation Learning)

    GRACE is an unsupervised graph representation learning framework that utilizes node-level contrastive objectives. It generates two views of a graph through 'corruption' (mixing structural and attribute-level perturbations) and learns node representations by maximizing the consistency between these two views. It provides theoretical grounding using mutual information and triplet loss.

  6. Overview of MGATS for Scientific Literature Summarization

    MGATS applies Graph Attention Networks (GATs) to the Multi-Perspective Scientific Literature Summarization (MuP) task, treating extractive summarization as a sentence ranking task. It generates a graph for each article based on the cosine similarity between sentence embeddings of the three most relevant sentences to the summary, using GATs to replace static normalization in GCNs.

  7. Overview of the Adaptive Graph Encoder (AGE) Framework

    The Adaptive Graph Encoder (AGE) is a framework for attributed graph embedding designed to learn vector representations from graph topology and node features. It addresses three primary limitations of existing Graph Convolutional Network (GCN) methods: the entanglement of filters and weight matrices, suboptimal low-pass characteristics of filters, and training objectives (like reconstructing adjacency matrices) that may not align with real-world applications.

    AGE consists of two main modules:

    1. Laplacian Smoothing Filter: Acts as a low-pass filter to remove high-frequency noise from the feature matrix.
    2. Adaptive Encoder: Iteratively enhances filtered features by adaptively selecting highly similar or dissimilar node pairs to build a training set for supervised training.

    The resulting node embeddings are used for downstream tasks such as node clustering and link prediction.

  8. Overview of GRACE: Deep Graph Contrastive Representation Learning

    GRACE is an unsupervised graph representation learning framework that utilizes node-level contrastive objectives. The core approach involves generating two augmented views of a graph through corruption and maximizing the consistency between the node representations of these two views. This allows the model to learn robust node embeddings without requiring labels, performing effectively in both transductive and inductive learning tasks.

  9. Overview of Deep Graph Infomax (DGI) for Unsupervised Graph Embedding

    Deep Graph Infomax (DGI), introduced at ICLR 2019, is an unsupervised graph embedding algorithm designed to overcome the limitations of random-walk based methods, which often overemphasize proximity at the expense of global structure and are highly sensitive to hyperparameters. DGI works by maximizing the mutual information between a local representation (patch) of a node and a corresponding global graph summary representation.

  10. Overview of DGI (Deep Graph Infomax)

    DGI is an unsupervised graph embedding algorithm that maximizes the mutual information between local node representations (patches) and a global graph summary. It creates 'corrupted' graphs by randomly permuting the feature matrix X while keeping the adjacency matrix A constant, then maximizes the similarity between positive node-graph pairs and minimizes it for negative pairs.

  11. Overview of MERIT (Multi-Scale Contrastive Siamese Networks)

    MERIT is a self-supervised method for graph representation learning that uses multi-scale contrastive learning and Siamese network self-distillation. It generates local and global augmented views of the input graph and maximizes consistency across two dimensions: cross-view (intra-view and inter-view scales) and cross-network.

  12. Overview of GUSUM (Graph-based Unsupervised Summarization)

    GUSUM is an unsupervised extractive document summarization method. It identifies important sentences by calculating sentence feature scores for vertex weights and using Sentence-BERT for semantic similarity to define edges in an undirected graph. It employs a ranking method on this graph to extract the most salient sentences while removing redundant, similar sentences.