Graph anomaly detection plays a vital role for identifying abnormal instances
in complex networks. Despite advancements of methodology based on deep learning
in recent years, existing benchmarking approaches exhibit limitations that
hinder a comprehensive comparison. In this paper, we revisit datasets and
approaches for unsupervised node-level graph anomaly detection tasks from three
aspects. Firstly, we introduce outlier injection methods that create more
diverse and graph-based anomalies in graph datasets. Secondly, we compare
methods employing message passing against those without, uncovering the
unexpected decline in performance associated with message passing. Thirdly, we
explore the use of hyperbolic neural networks, specifying crucial architecture
and loss design that contribute to enhanced performance. Through rigorous
experiments and evaluations, our study sheds light on general strategies for
improving node-level graph anomaly detection methods.

本文针对复杂网络中识别异常实例的图异常检测问题，从深度学习方法学、基准评估方法及超几何神经网络等三个方面展开研究，通过研究发现了改进节点级图异常检测方法的一般策略。

节点级图异常检测的三个重访：异常值、消息传递和双曲神经网络

Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message  Passing and Hyperbolic Neural Networks

Large language models (LLMs) can memorize many pretrained sequences verbatim.
This paper studies if we can locate a small set of neurons in LLMs responsible
for memorizing a given sequence. While the concept of localization is often
mentioned in prior work, methods for localization have never been
systematically and directly evaluated; we address this with two benchmarking
approaches. In our INJ Benchmark, we actively inject a piece of new information
into a small subset of LLM weights and measure whether localization methods can
identify these "ground truth" weights. In the DEL Benchmark, we study
localization of pretrained data that LLMs have already memorized; while this
setting lacks ground truth, we can still evaluate localization by measuring
whether dropping out located neurons erases a memorized sequence from the
model. We evaluate five localization methods on our two benchmarks, and both
show similar rankings. All methods exhibit promising localization ability,
especially for pruning-based methods, though the neurons they identify are not
necessarily specific to a single memorized sequence.

本研究主要研究在大型语言模型中是否可以确定负责记忆给定序列的少量神经元，并通过两种基准方法进行了评估。评估结果显示，尽管所识别的神经元不一定与单个记忆序列相关，但所有方法都展现出令人满意的定位能力，特别是基于修剪的方法。

LLM 中的本地化方法是否真正本地化记忆数据？

Do Localization Methods Actually Localize Memorized Data in LLMs?

Data-driven science is an emerging paradigm where scientific discoveries
depend on the execution of computational AI models against rich,
discipline-specific datasets. With modern machine learning frameworks, anyone
can develop and execute computational models that reveal concepts hidden in the
data that could enable scientific applications. For important and widely used
datasets, computing the performance of every computational model that can run
against a dataset is cost prohibitive in terms of cloud resources. Benchmarking
approaches used in practice use representative datasets to infer performance
without actually executing models. While practicable, these approaches limit
extensive dataset profiling to a few datasets and introduce bias that favors
models suited for representative datasets. As a result, each dataset's unique
characteristics are left unexplored and subpar models are selected based on
inference from generalized datasets. This necessitates a new paradigm that
introduces dataset profiling into the model selection process. To demonstrate
the need for dataset-specific profiling, we answer two questions:(1) Can
scientific datasets significantly permute the rank order of computational
models compared to widely used representative datasets? (2) If so, could
lightweight model execution improve benchmarking accuracy? Taken together, the
answers to these questions lay the foundation for a new dataset-aware
benchmarking paradigm.

数据驱动科学是一种新兴的范例，其中科学发现取决于针对具体学科的丰富数据集执行计算 AI 模型。通过现代机器学习框架，任何人都可以开发和执行计算模型，揭示隐藏在数据中的概念，从而可能支持科学应用。然而，在实际应用中，收集并计算每个可以运行的计算模型的性能代价昂贵。由于使用代表性数据集来推断性能的基准测试方法具有局限性，每个数据集都具有独特的特征，这需要引入数据集配置文件来作为模型选择过程的一部分，以选择最佳模型进行优化。