Wensheng Luo

Wensheng Luo, Chenhao Ma, Yixiang Fang et al.

With the prevalence of graphs for modeling complex relationships among objects, the topic of graph mining has attracted a great deal of attention from both academic and industrial communities in recent years. As one of the most fundamental problems in graph mining, the densest subgraph discovery (DSD) problem has found a wide spectrum of real applications, such as discovery of filter bubbles in social media, finding groups of actors propagating misinformation in social media, social network community detection, graph index construction, regulatory motif discovery in DNA, fake follower detection, and so on. Theoretically, DSD closely relates to other fundamental graph problems, such as network flow and bipartite matching. Triggered by these applications and connections, DSD has garnered much attention from the database, data mining, theory, and network communities. In this survey, we first highlight the importance of DSD in various real-world applications and the unique challenges that need to be addressed. Subsequently, we classify existing DSD solutions into several groups, which cover around 50 research papers published in many well-known venues (e.g., SIGMOD, PVLDB, TODS, WWW), and conduct a thorough review of these solutions in each group. Afterwards, we analyze and compare the models and solutions in these works. Finally, we point out a list of promising future research directions. It is our hope that this survey not only helps researchers have a better understanding of existing densest subgraph models and solutions, but also provides insights and identifies directions for future study.

Tianyu Sima, Mingyu Yan, Jianfeng Wen et al.

The transportation network operator (TNO) and the power distribution network operator (DNO) act non cooperatively during the scheduling process. Under the TNOs management, the distribution of charging load may exacerbate the local supply-demand imbalance in the power distribution network (PDN), which negatively impacts the secure and economic operation of the PDN. This paper proposes a profit sharing mechanism based on the principle of incentive compatibility for coordinating the transportation network (TN) and the PDN to minimize the total operation cost of the PDN. In this mechanism, the scheduling process of the power transportation system is divided into two stages. At the prescheduling stage, the TNO allocates traffic flow and charging load without considering the operation of the PDN, after which the DNO schedules and obtains the original cost. At the rescheduling stage, the DNO shares part of the saved dispatch cost to motivate the TNO to reallocate the EVs charging, which is more beneficial to the operation of the PDN. This two-stage process is then simulated by two single level models and a bilevel model. Finally, the optimal sharing ratio is identified, at which the total scheduling cost of the DNO can decrease to the lowest point when gaming with the TNO. The efficiency of the proposed mechanism is simulated via a coupled network with 12 traffic nodes and 18 electric buses. Numerical results demonstrate that the DNO can achieve the minimum total cost. Simultaneously, the TNO can also benefit from the proposed profit-sharing mechanism.

Meng Song, Xinyi Jing, Jianyong Ding et al.

Virtual energy stations (VESs) work as retailers to provide electricity and natural gas sale services for integrated energy systems (IESs), and guide IESs energy consumption behaviors to tackle the varying market prices via integrated demand response (IDR). However, IES customers are risk averse and show low enthusiasm in responding to the IDR incentive signals. To address this problem, exergy is utilized to unify different energies and allowed to be virtually stored and withdrawn for arbitrage by IESs. The whole incentive mechanism operating process is innovatively characterized by a virtual exergy battery. Peer to peer (P2P) exergy trading based on shared exergy storage is also developed to reduce the energy cost of IESs without any extra transmission fee. In this way, IES can reduce the economic loss risk caused by the market price fluctuation via the different time (time dimension), multiple energy conversion (energy dimension), and P2P exergy trading (space dimension) arbitrage. Moreover, the optimal scheduling of VES and IESs is modeled by a bilevel optimization model. The consensus based alternating direction method of multipliers (CADMM) algorithm is utilized to solve this problem in a distributed way. Simulation results validate the effectiveness of the proposed incentive mechanism and show that the shared exergy storage can enhance the benefits of different type IESs by 18.96%, 3.49%, and 3.15 %, respectively.

Fangyuan Zhang, Zhengjun Huang, Yingli Zhou et al.

Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official.