The analysis of (social) networks and multi-agent systems is a central theme
in Artificial Intelligence. Some line of research deals with finding groups of
agents that could work together to achieve a certain goal. To this end,
different notions of so-called clusters or communities have been introduced in
the literature of graphs and networks. Among these, defensive alliance is a
kind of quantitative group structure. However, all studies on the alliance so
for have ignored one aspect that is central to the formation of alliances on a
very intuitive level, assuming that the agents are preconditioned concerning
their attitude towards other agents: they prefer to be in some group (alliance)
together with the agents they like, so that they are happy to help each other
towards their common aim, possibly then working against the agents outside of
their group that they dislike. Signed networks were introduced in the
psychology literature to model liking and disliking between agents,
generalizing graphs in a natural way. Hence, we propose the novel notion of a
defensive alliance in the context of signed networks. We then investigate
several natural algorithmic questions related to this notion. These, and also
combinatorial findings, connect our notion to that of correlation clustering,
which is a well-established idea of finding groups of agents within a signed
network. Also, we introduce a new structural parameter for signed graphs,
signed neighborhood diversity snd, and exhibit a parameterized algorithm that
finds a smallest defensive alliance in a signed graph.

在这篇论文中，我们介绍了在社交网络和多智能体系统中分析防御联盟的概念，探讨了形成联盟的因素，并提出了一种基于有向网络的防御联盟的新概念，并研究了与之相关的算法问题和组合发现。此外，我们还引入了一种新的有向图结构参数，并提出了一个参数化算法，能够在有向图中找到最小的防御联盟。