Heterogeneous Graph Structure Learning for Graph Neural Networks
Motivation：
现有的异构图学习中一些图的结构是不好的，不利于下游任务，比如在user-item的异构图中，用户可能会点击一些无用的item，从而给异构图表示学习带来一些噪声。
现有有的一些图结构表示学习的方法，通过把邻接矩阵参数化，学习更好的邻接矩阵用于下游任务。但是他们都是用于同构图的 ...
How to crack a soga backend
How to crack a soga backendSoga 后端破解思路小分享。
仅以此文记录和分享一些关于go应用破解的过程。由于本人也是第一次进行go程序的逆向，本文的分析会比较简单和基础。
Sogasoga 后端是一个同时支持 v2ray、Trojan、Shadowsocks 的后端，社区 ...
Beyond Clicks Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
BackgroundSession recommendation:
Aiming to predict the next item to be interacted with a user under a specific type of behavior, and modeling user d ...
GHashing Semantic Graph Hashing for Approximate Similarity Search in Graph Databases
Objection：
Retrieve graphs from the database similar enough to a query.
Optimize the pruning stage.
Background:
Graph Edit Distance (GED) is a me ...
Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning
The problem of GNNs:
传统GNN会被1-WL test 所限制。因为节点都是以度进行区分的。
核心问题：节点分类或者连接预测并不是同构问题，但是GNN是基于WL-test的所以必须要给节点引入特征。
传统的WLtest会根据节点的度来区分节点，就会导致无法区分结构信 ...
Heterogeneous Deep Graph Infomax
Heterogeneous Deep Graph InfomaxAbstractInspired by the emerging mutual information-based learning algorithm, This paper propose an unsupervised graph ...
Redundancy-Free Computation for Graph Neural Networks
Redundancy-Free Computation for Graph Neural NetworksMotivation
To avoid redundant computations：减少冗余计算
HAGs are functionally equivalent to standard G ...