address concerns

docs/index.rst

@@ -133,7 +133,7 @@ Now, you can import and use GraphSL in your Python code.
   
 
 
-That's it! You're ready to start using GraphSL. You can check results on the Jupyter notebook tutorial.ipynb from the repo (https://github.com/xianggebenben/GraphSL).
+That's it! You're ready to start using GraphSL. You can check results on the Jupyter notebook tutorial.ipynb from the repo.
 
 If you use this package in your research, please consider citing our work as follows:
 

paper.md

@@ -34,9 +34,9 @@ We introduce GraphSL, a new library for studying the graph source localization p
 
 ![An example of graph source localization.\label{fig:example}](SL_example.png)
 
-Graph diffusion is a fundamental task in graph learning, which aims to predict future information diffusions given information sources. Conversely, its inverse problem, graph source localization, though rarely explored, stands as an extremely important topic: it focuses on the detection of information sources given their future information diffusions. As illustrated in \autoref{fig:example}, graph diffusion seeks to predict the information diffusion $\{b,c,d,e\}$ from a source node $b$, whereas graph source localization aims to identify the source node $b$ from the information diffusion $\{b,c,d,e\}$. Graph source localization spans a broad spectrum of promising research and real-world applications such as rumor detection [@evanega2020coronavirus], tracking of sources for computer viruses, [@kephart1993measuring], and failures detection in smart grids [@amin2007preventing]. Please refer to the survey paper [@jiang2016identifying] for more information. Hence, the graph source localization problem demands attention and extensive investigations from machine learning researchers.
+Graph diffusion is a fundamental task in graph learning, which aims to predict future information diffusions given information sources. Its inverse problem is graph source localization, which is an extremely important topic even though rarely explored: it focuses on the detection of information sources given their future information diffusions. As illustrated in \autoref{fig:example}, graph diffusion seeks to predict the information diffusion $\{b,c,d,e\}$ from a source node $b$, whereas graph source localization aims to identify the source node $b$ from the information diffusion $\{b,c,d,e\}$. Graph source localization spans a broad spectrum of promising research and real-world applications such as rumor detection [@evanega2020coronavirus], tracking of sources for computer viruses[@kephart1993measuring], and failure detection in smart grids [@amin2007preventing]. Please refer to the survey paper [@jiang2016identifying] for more information. Hence, the graph source localization problem demands attention and extensive investigations from machine learning researchers.
 
-Due to its importance, some open-source tools have been developed to support the research of the graph source localization problem. Two recent examples are cosasi [@McCabe2022joss] and RPaSDT [@frkaszczak2022rpasdt]. However, they do not support various simulations of information diffusion, and they also miss real-world benchmark datasets and state-of-the-art source localization approaches. To fill this gap, we propose a new library GraphSL: the first one to include real-world benchmark datasets and recent source localization methods to our knowledge, enabling researchers and practitioners to evaluate novel techniques against appropriate baselines easily. These methods do not require prior knowledge (e.g. single source or multiple sources) and can handle graph source localization based on various diffusion simulation models such as Independent Cascade (IC) and Linear Threshold (LT) [@shakarian2015independent]. Our GraphSL library is standardized: for instance, tests of all source inference methods return a Metric object, which provides five performance metrics (accuracy, precision, recall, F-score, and area under ROC curve) for performance evaluation.
+Due to its importance, some open-source tools have been developed to support the research of the graph source localization problem. Two recent examples are cosasi [@McCabe2022joss] and RPaSDT [@frkaszczak2022rpasdt]. However, they do not support various simulations of information diffusion, and they also miss real-world benchmark datasets and state-of-the-art source localization approaches. To fill this gap, we propose a new library GraphSL: the first one to include real-world benchmark datasets and recent source localization methods to our knowledge, enabling researchers and practitioners to evaluate novel techniques against appropriate baselines easily. These methods do not require prior assumptions about the source (e.g. single source or multiple sources) and can handle graph source localization based on various diffusion simulation models such as Independent Cascade (IC) and Linear Threshold (LT) [@shakarian2015independent]. Our GraphSL library is standardized: for instance, tests of all source inference methods return a Metric object, which provides five performance metrics (accuracy, precision, recall, F-score, and area under ROC curve) for performance evaluation.
 
 Our proposed GraphSL library targets both developers and practical users: they are free to add algorithms and datasets for personal needs by following the guidelines in the "Contact" section of [README.md](https://github.com/xianggebenben/GraphSL/blob/main/README.md).
 
@@ -46,7 +46,8 @@ Our proposed GraphSL library targets both developers and practical users: they a
 
 The structure of our GraphSL library is depicted in \autoref{fig:overview}. Existing methods can be categorized into two groups: Prescribed methods and Graph Neural Networks (GNN)-based methods.
 
-Prescribed methods rely on hand-crafted rules and heuristics. For instance, LPSI propagates infection in networks and labels local peaks as source nodes [@wang2017multiple]. NetSleuth employs the Minimum Description Length principle to identify the optimal set of source nodes and virus propagation ripple [@prakash2012spotting]. OJC identifies a set of nodes (Jordan cover) that cover all observed infected nodes with the minimum radius [@zhu2017catch].
+Prescribed methods rely on hand-crafted rules and heuristics. For instance, LPSI assumes that nodes surrounded by larger proportions
+of infected nodes are more likely to be source nodes [@wang2017multiple]. NetSleuth employs the Minimum Description Length principle to identify the optimal set of source nodes and virus propagation ripple [@prakash2012spotting]. OJC identifies a set of nodes (Jordan cover) that cover all observed infected nodes with the minimum radius [@zhu2017catch].
 
 GNN-based methods learn rules from graph data in an end-to-end manner by capturing graph topology and neighboring information. For example, GCNSI utilizes LPSI to enhance input and then applies Graph Convolutional Networks (GCN) for source identification [@dong2019multiple]; IVGD introduces a graph residual scenario to make existing graph diffusion models invertible, and it devises a new set of validity-aware layers to project inferred sources to feasible regions [@IVGD_www22]. SLVAE uses forward diffusion estimation and deep generative models to approximate source distribution, leveraging prior knowledge for generalization under arbitrary diffusion patterns [@ling2022source].