Graph-RAG constructs a knowledge graph from text chunks to improve retrieval in Large Language Model (LLM)-based question answering. It is particularly useful in domains such as biomedicine, law, and political science, where retrieval often requires multi-hop reasoning over proprietary documents. Some existing Graph-RAG systems construct KNN graphs based on text chunk relevance, but this coarse-grained approach fails to capture entity relationships within texts, leading to sub-par retrieval and generation quality. To address this, recent solutions leverage LLMs to extract entities and relationships from text chunks, constructing triplet-based knowledge graphs. However, this approach incurs significant indexing costs, especially for large document collections.

Influence maximization (IM) is a classic problem that aims to identify a small group of critical individuals, known as seeds, who can influence the largest number of users in a social network through word-of-mouth. This problem finds important applications including viral marketing, infection detection, and misinformation containment. The conventional IM problem is typically studied with the oversimplified goal of selecting a single seed set. Many real-world scenarios call for multiple sets of seeds, particularly on social media platforms where various viral marketing campaigns need different sets of seeds to propagate effectively. To this end, previous works have formulated various IM variants, central to which is the requirement of multiple seed sets, naturally modeled as a matroid constraint. However, the current best-known solutions for these variants either offer a weak (1/2 - ε )-approximation, or offer a (1 - 1/ e - ε )-approximation algorithm that is very expensive. We propose an efficient seed selection method called AMP, an algorithm with a (1 - 1/ e - ε )-approximation guarantee for this family of IM variants. To further improve efficiency, we also devise a fast implementation, called RAMP. We extensively evaluate the performance of our proposal against 6 competitors across 4 IM variants and on 7 real-world networks, demonstrating that our proposal outperforms all competitors in terms of result quality, running time, and memory usage. We have also deployed RAMP in a real industry strength application involving online gaming, where we show that our deployed solution significantly improves upon the baselines.

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the 1/2-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with (1/2-ε)-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.