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class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page group hide" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> 列表</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> 音乐</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> 电影</span></a></li><li><a class="site-page child" href="/Gallery/"><i class="fa-fw fas fa-images"></i><span> 图库</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友链</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> 关于</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="full_page" id="page-header" style="background-image: url('/image/w1.png')"><nav id="nav"><span id="blog-info"><a href="/" title="JJXxXLL"><span class="site-name">JJXxXLL</span></a></span><div id="menus"><div id="search-button"><a class="site-page social-icon search" href="javascript:void(0);"><i class="fas fa-search fa-fw"></i></a></div><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page group hide" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> 列表</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> 音乐</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> 电影</span></a></li><li><a class="site-page child" href="/Gallery/"><i class="fa-fw fas fa-images"></i><span> 图库</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友链</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> 关于</span></a></div></div><div id="toggle-menu"><a class="site-page" href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">JJXxXLL</h1><div id="site-subtitle"><span id="subtitle"></span></div><div id="site_social_icons"><a class="social-icon" href="https://github.com/JJxXxLL" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:[email protected]" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/22/%E7%9F%A9%E9%98%B5%E7%90%86%E8%AE%BA%E5%A4%8D%E4%B9%A01/" title="矩阵理论复习1">矩阵理论复习1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-22T05:12:12.000Z" title="发表于 2024-12-22 13:12:12">2024-12-22</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">1.2
1.4
投影
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/22/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A05/" title="体系结构复习5">体系结构复习5</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-22T02:12:12.000Z" title="发表于 2024-12-22 10:12:12">2024-12-22</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">第5章 存储系统存储器的分类和主要特点Ø半导体存储器
Ø磁表面存储器
Ø光盘存储器
存储器存取方式:Ø随机存取存储器(RAM)Ø顺序存取存储器(SAM)
利用局部性原理：
. 以最便宜技术提供尽可能多的存储空间
. 以最快的技术提供访问
Cache的三种映像关系：全相联、直接映像、组相联
主存地址Tag、Index、块内偏移三个字段的计算Cache块的替换策略
Cache的读写过程
平均访存时间和CPU时间的计算
Cache失效率的类别，以及每种失效率的解决方法有哪些 按照产生失效的原因不同，可以把失效分为以下3类（简称为“3C”）：
强制性失效（Compulsory Miss）：当第一次访问一个块时，该块不在Cache中，需从下一级存储器中调入Cache，这就是强制性失效。这种失效也称为首次访问失效。
解决方法：减少强制性失效，可以增加块的大小。
容量失效（Capacity Miss）：如果Cache容纳不了一个程序执行所需要的所有块，将会发生容量失效（还会发生强制失效），某些块将被丢弃，随后再被调入
解决方法：要减小容量失效，可以增大Cac ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/21/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A04/" title="体系结构复习4">体系结构复习4</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-21T02:12:12.000Z" title="发表于 2024-12-21 10:12:12">2024-12-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">第4章 流水线技术及指令级并行1.流水线的概念、分类
概念：流水线是利用执行指令操作之间的并行性，实现多条指令重叠执行的技术。
特点：
段数（Stage）：流水线分为多个阶段，每阶段完成指令的部分操作。
吞吐量：单位时间内流水线输出的指令数量。
瓶颈段：流水线中耗时最长的段，决定了整体性能。
分类：
按时间是否均匀：
均匀流水线：每段时间相等。
非均匀流水线：段时间不等。
按数据类型：
标量流水线：处理标量数据。
向量流水线：处理向量数据。
按功能：
单功能流水线：实现单一功能（如浮点运算）。
多功能流水线：各段可进行不同操作。
按工作方式：
静态流水线：每段连接固定。
动态流水线：各段连接可变，控制更复杂。
按规模：
操作流水线
指令流水线
宏流水线
连接方式
线性流水线
非线性流水线
解决访存冲突的办法：
1)将主存分为两个独立编址的存储器：指令存储器和数据存储器，CPU可分别独立访问。
2)低位交叉存取方式：可并行访问不在同一个存储体中的指令或数据。
3)指令预取：在重叠操作中，当前一条指令在执行过程中就需要提前取出后面的指令进 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/20/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A03/" title="体系结构复习3">体系结构复习3</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-20T07:12:12.000Z" title="发表于 2024-12-20 15:12:12">2024-12-20</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">第3章 单周期MIPS处理器的设计1.add, sub, addi, subi, lw, sw, beq, j 每条指令在单周期处理器中的执行逻辑MIPS单周期处理器中各指令的执行逻辑以下是 add、sub、addi、subi、lw、sw、beq 和 j 指令在单周期处理器中的执行逻辑，包括各个阶段和数据通路的工作流程。
1. R型指令：add 和 subR型指令用于寄存器之间的算术逻辑操作。
格式：
\text{[op (6位)][rs (5位)][rt (5位)][rd (5位)][shamt (5位)][funct (6位)]}
示例：
add rd, rs, rt：\text{rd} = \text{rs} + \text{rt}
sub rd, rs, rt：\text{rd} = \text{rs} - \text{rt}
执行逻辑
取指（Instruction Fetch）：从指令存储器中取出指令，PC更新为 PC + 4。
译码（Instruction Decode）：读取指令中的寄存器地址字段：rs、rt 和 rd，从寄存器堆中读取 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/19/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A02/" title="体系结构复习2">体系结构复习2</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-19T06:12:12.000Z" title="发表于 2024-12-19 14:12:12">2024-12-19</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">第2章 指令系统原理与示例1.指令集系统结构的分类：指令集系统的不同结构根据课件内容，指令集系统结构（Instruction Set Architecture, ISA） 的分类可以根据处理器内部数据的存储类型和操作数访问方式进行划分。
1. 根据处理器内部数据存储类型分类指令集系统结构的最根本区别在于处理器内部数据的存储类型，主要分为以下三类：
(1) 堆栈系统结构（Stack Architecture）
特点：
操作数隐含地位于栈顶，不需要显式指定。
操作数通过 push 和 pop 指令进行堆栈操作。
优点：
指令长度较短（因为操作数隐含）。
缺点：
数据操作效率较低，需要频繁的堆栈存取。
操作顺序受到栈顶限制（无法乱序操作）。
(2) 累加器系统结构（Accumulator Architecture）
特点：
一个操作数隐含在累加器中，另一个操作数需要显式指定。
计算结果存储在累加器中。
优点：
指令长度较短。
简单易实现，适用于早期计算机。
缺点：
累加器的内容需要频繁存取内存，导致效率低。
(3) 通用寄存器系统结构（General Purp ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/18/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A01/" title="体系结构复习1">体系结构复习1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-18T05:12:12.000Z" title="发表于 2024-12-18 13:12:12">2024-12-18</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">第1章 量化设计与分析基础1.计算机的分类类别 Flynn分类法（基于指令流和数据流数量）
SISD (Single Instruction Single Data)
单指令单数据流
适用于单处理器系统（Uniprocessors）。
MISD (Multiple Instruction Single Data)
多指令单数据流
特殊情况，通常不常见，具体实例较少（???）。
SIMD (Single Instruction Multiple Data)
单指令多数据流
适合并行处理，常用于向量处理器和图形处理。
示例：Illiac-IV、CM-2。
MIMD (Multiple Instruction Multiple Data)
多指令多数据流
常用于多核处理器和并行计算。
示例：SPARCCenter、T3D。
2.计算机系统结构定义和计算机的设计任务：指令集结构概念及要素计算机系统结构（现代定义）：是在满足功能、性能和价格目标的条件下，设计、选择和互连硬件部件构成计算机。
系统结构覆盖：
指令系统设计 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/12/14/PMP/" title="Partitioning Message Passing for Graph Fraud Detection">Partitioning Message Passing for Graph Fraud Detection</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-14T11:42:32.000Z" title="发表于 2024-12-14 19:42:32">2024-12-14</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%96%87%E7%8C%AE%E5%AD%A6%E4%B9%A0/">文献学习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B/">图异常检测</a></span></div><div class="content">文献地址：PMP.pdf
代码仓库：Xtra-Computing/PMP
1.背景及创新点1. 问题背景与挑战在使用图神经网络进行图欺诈检测时，主要面临两个挑战：
标签不平衡（Label Imbalance）
图数据通常存在标签不平衡的问题，即欺诈和非欺诈的节点标签数量差异很大。例如，在一个社交网络中，可能大多数节点代表的是正常用户，只有少部分节点代表欺诈用户。这种不平衡的标签分布会导致模型对少数类（欺诈类）预测不准确，从而影响整体检测效果。
同质性-异质性混合（Homophily-Heterophily Mixture）
图数据中存在着同质性（Homophily）和异质性（Heterophily）的混合关系：
同质性指的是图中节点之间具有相似的属性或标签，通常是图神经网络的假设基础（例如，社交网络中的好友节点往往有相似的兴趣）。
异质性则指的是图中某些节点之间存在明显的差异，例如不同类别或标签的节点连接在一起。
传统的图神经网络模型倾向于利用同质性关系，忽略异质性节点的贡献，但在实际的图欺诈检测任务中，异质性节点的信息同样重要，因为欺诈节点往往和非欺诈节点之间有着不同的行为模式或 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/11/24/%E8%B4%AA%E5%BF%83%E7%AE%97%E6%B3%95/" title="贪心算法复习">贪心算法复习</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-24T15:42:32.000Z" title="发表于 2024-11-24 23:42:32">2024-11-24</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E8%80%83%E8%AF%95%E5%A4%8D%E4%B9%A0/">考试复习</a></span></div><div class="content">贪心算法1. 贪心算法 (Greedy Algorithm)
基本思想：在每一步中选择“当前最优解”，即局部最优解，期望通过一系列局部最优选择达到全局最优。
局部最优与全局最优的关系：
贪心算法仅在某些特定问题中有效，即局部最优选择能导出全局最优解。
贪心算法通常简单且高效，但并不总是正确。
优点：算法简单、执行效率高。
缺点：可能产生错误结果或次优解。
1.1案例：区间调度问题 (Interval Scheduling)
问题描述：
给定 $n$ 个任务，每个任务有一个开始时间 $s_j$ 和结束时间 $f_j$。
两个任务相容当且仅当它们不重叠。
目标：选择最多数量的相容任务。
贪心策略：
按任务的结束时间 $f_j$ 升序排序。
每次选择当前最早结束且与已选任务不冲突的任务。
时间复杂度：
排序：$O(n \log n)$。
遍历：$O(n)$。
总时间复杂度：$O(n \log n)$。
以下是区间调度问题 (Interval Scheduling) 的贪心算法伪代码：
区间调度问题的伪代码123456789101112131415Algorithm Int ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/11/24/RQGNN/" title="Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection Detection">Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection Detection</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-24T05:42:32.000Z" title="发表于 2024-11-24 13:42:32">2024-11-24</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%96%87%E7%8C%AE%E5%AD%A6%E4%B9%A0/">文献学习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B/">图异常检测</a></span></div><div class="content">Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection文献地址：Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection | OpenReview
代码仓库：xydong127/RQGNN
1 问题核心问题是：如何通过光谱特性有效地进行图级异常检测。
1.1 现有问题
光谱特性的忽视：
当前图异常检测方法多基于空间域的特征，例如节点属性和拓扑信息，未能充分利用图的光谱特性。
异常图和正常图在光谱能量分布上存在显著差异，但这一特性尚未被现有方法利用。
模型解释性不足：
许多现有方法框架复杂，设计上缺乏理论依据，难以解释异常检测的核心原理。
性能不足：
当前模型在异常检测任务中效果有限，特别是在数据不平衡的情况下难以有效捕捉异常模式。
1.2 文献中的解决方案针对上述问题，本文提出了一个创新性的解决方案：
通过研究Rayleigh商，揭示正常图和异常图的累积光谱能量分布存在统计学差异。
利用光谱 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/11/13/HOGRL/" title="Effective High-order Graph Representation Learning for Credit Card Fraud Detection">Effective High-order Graph Representation Learning for Credit Card Fraud Detection</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-13T15:42:32.000Z" title="发表于 2024-11-13 23:42:32">2024-11-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%96%87%E7%8C%AE%E5%AD%A6%E4%B9%A0/">文献学习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B/">图异常检测</a></span></div><div class="content">论文地址：Effective High-order Graph Representation Learning for Credit Card Fraud Detection
代码仓库：AI4Risk/antifraud: A repository for financial fraud detection
背景，动机，创新1 背景随着数字支付的普及，信用卡欺诈已成为全球金融系统中的重大问题，带来巨大的经济损失，并影响个人和企业的财务安全。报告预测未来十年全球信用卡欺诈损失将达数千亿美元，因此有效的欺诈检测技术对于维持金融系统的健康至关重要。
2 动机
现有的欺诈检测方法，包括基于规则和传统的机器学习方法，难以识别复杂的欺诈行为，特别是那些伪装性强的间接交易。在这种情况下，欺诈者会通过多个正常用户和合法交易掩盖其身份，绕过检测系统，使得欺诈交易看起来像普通的多跳间接交易。虽然近年来图神经网络（GNN）因其在捕捉节点间关系方面的优势而被应用于欺诈检测，但现有的GNN方法在识别这种伪装交易时效果有限。主要原因在于：
伪装欺诈的复杂性：欺诈者往往利用多跳间接交易来隐藏自己，使得直接邻居大多 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/11/07/GADRA/" title="GADRA-Graph Anomaly Detection via Neighborhood Reconstruction">GADRA-Graph Anomaly Detection via Neighborhood Reconstruction</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-07T10:46:35.000Z" title="发表于 2024-11-07 18:46:35">2024-11-07</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%96%87%E7%8C%AE%E5%AD%A6%E4%B9%A0/">文献学习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B/">图异常检测</a></span></div><div class="content">GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction论文地址：GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
代码仓库：Graph-COM/GAD-NR: [WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
关键词：Anomaly Detection, Graph Neural Network, Auto-Encoder
1.介绍1.1 背景现有的图自编码器（GAE）方法通过将图数据编码为节点表示，然后评估图的重建质量来检测异常。然而，现有的GAE模型主要优化的是直接连接的重建，导致在处理复杂结构的异常时效果较差，尤其是那些不符合簇型结构的异常节点。
为了解决这个问题，本文提出了一种新的方法——GAD-NR，即基于邻域重建的图异常检测方法。GAD-NR不仅重建节点之间的连接，还通过邻域重建来评估节点的异常性，考虑节点的局部结构、节点自 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/11/01/DAGAD/" title="DAGAD-Data Augmentation for Graph Anomaly Detection">DAGAD-Data Augmentation for Graph Anomaly Detection</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-01T10:23:41.000Z" title="发表于 2024-11-01 18:23:41">2024-11-01</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%96%87%E7%8C%AE%E5%AD%A6%E4%B9%A0/">文献学习</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B/">图异常检测</a></span></div><div class="content">文献地址：DAGAD: Data Augmentation for Graph Anomaly Detection | IEEE Conference Publication | IEEE Xplore
1 介绍1.1 问题文章研究了在图数据中检测异常节点的技术。现有方法面临两个主要问题：
异常样本稀缺：由于异常行为通常较为隐蔽，且缺少足够的背景知识，因此难以有效捕捉异常样本。
类别不平衡：现实中的图数据中，大部分对象是正常节点，异常节点数量极少，导致类别不平衡的问题。
为了解决这些问题，论文提出了一种基于数据增强的图异常检测框架，称为DAGAD（Data Augmentation-based Graph Anomaly Detection）。这个框架包括三个模块：
信息融合模块：利用图神经网络编码器学习节点的表示。
数据增强模块：通过生成额外的训练样本来扩充训练集。
不平衡学习模块：优化少数类别（异常类）和多数类别（正常类）之间的分布差异。
1.2 贡献本文在图异常检测领域的贡献包括以下几个方面：
所提出的图数据增强技术在嵌入空间中从原始训练集生成附加样本。通过两种分类器 ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">…</span><a class="page-number" href="/page/4/#content-inner">4</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="/image/1.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">JJxXxLL</div><div class="author-info__description">记录学习</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">42</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">11</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">3</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/JJxXxLL"><i class="fa-solid fa-eye"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/JJxXxLL" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:[email protected]" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">欢迎来到我的博客！</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/12/22/%E7%9F%A9%E9%98%B5%E7%90%86%E8%AE%BA%E5%A4%8D%E4%B9%A01/" title="矩阵理论复习1">矩阵理论复习1</a><time datetime="2024-12-22T05:12:12.000Z" title="发表于 2024-12-22 13:12:12">2024-12-22</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/12/22/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A05/" title="体系结构复习5">体系结构复习5</a><time datetime="2024-12-22T02:12:12.000Z" title="发表于 2024-12-22 10:12:12">2024-12-22</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/12/21/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A04/" title="体系结构复习4">体系结构复习4</a><time datetime="2024-12-21T02:12:12.000Z" title="发表于 2024-12-21 10:12:12">2024-12-21</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/12/20/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A03/" title="体系结构复习3">体系结构复习3</a><time datetime="2024-12-20T07:12:12.000Z" title="发表于 2024-12-20 15:12:12">2024-12-20</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/12/19/%E4%BD%93%E7%B3%BB%E7%BB%93%E6%9E%84%E5%A4%8D%E4%B9%A02/" title="体系结构复习2">体系结构复习2</a><time datetime="2024-12-19T06:12:12.000Z" title="发表于 2024-12-19 14:12:12">2024-12-19</time></div></div></div></div></div></div></main><footer id="footer"><div id="footer-wrap"><div class="copyright">©2024 By JJxXxLL</div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button><button id="go-up" type="button" title="回到顶部"><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/[email protected]/dist/fancybox/fancybox.umd.min.js"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/instantpage.min.js" type="module"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/lazyload.iife.min.js"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/snackbar.min.js"></script><script>function panguFn () {
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