Within the network analysis field, network immunization refers to the task of
protecting a network from some arbitrary diffusion that tries to infect it. In
this article, we consider the spread of harmful content in social networks, and
we propose CONTAIN, a novel COmmuNiTy-based Algorithm for network ImmuNization.
Our solution uses the network information to (1) detect harmful content
spreaders, and (2) generate partitions and rank them for immunization using the
subgraphs induced by each spreader, i.e., employing CONTAIN. The experimental
results obtained on real-world datasets show that CONTAIN outperforms
state-of-the-art solutions, i.e., NetShield and SparseShield, by immunizing the
network in fewer iterations, thus, converging significantly faster than the
state-of-the-art algorithms.

该论文提出了一种基于社区的 COmmuNiTy-based Algorithm，称为 CONTAIN，用于检测社交网络中的有害内容传播者，从而保护网络免受任意扩散的影响，并通过实验证明其比现有的算法快速收敛。

基于社区的网络免疫算法

CONTAIN: A Community-based Algorithm for Network Immunization

The widespread availability of internet access and handheld devices confers
to social media a power similar to the one newspapers used to have. People seek
affordable information on social media and can reach it within seconds. Yet
this convenience comes with dangers; any user may freely post whatever they
please and the content can stay online for a long period, regardless of its
truthfulness. A need to detect untruthful information, also known as fake news,
arises. In this paper, we present an end-to-end solution that accurately
detects fake news and immunizes network nodes that spread them in real-time. To
detect fake news, we propose two new stack deep learning architectures that
utilize convolutional and bidirectional LSTM layers. To mitigate the spread of
fake news, we propose a real-time network-aware strategy that (1) constructs a
minimum-cost weighted directed spanning tree for a detected node, and (2)
immunizes nodes in that tree by scoring their harmfulness using a novel ranking
function. We demonstrate the effectiveness of our solution on five real-world
datasets.

本研究提出了一种端到端解决方案，通过卷积和双向 LSTM 层提出两种新型深度学习架构来准确检测虚假新闻，并提供了一种实时的网络感知策略，以构建最小成本加权有向生成树来抑制虚假新闻的传播，并利用新型排名函数对这些节点进行免疫评分，实验在五个真实数据集上证明了我们的解决方案的有效性。