Influencer marketing campaign success heavily depends on identifying key opinion leaders who can effectively leverage their credibility and reach to promote products or services.The selection of influencers is vital for boosting brand visibility, fostering consumer trust, and driving sales.While traditional research often simplifies complex factors like user attitudes, interaction frequency, and advertising content, into simple numerical values.However, this reductionist approach fails to capture the dynamic nature of influencer marketing effectiveness.To bridge this gap, we present SAGraph, a novel comprehensive dataset from Weibo that captures multi-dimensional marketing campaign data across six product domains.The dataset encompasses 345,039 user profiles with their complete interaction histories, including 1.3M comments and 554K reposts across 44K posts, providing unprecedented granularity in influencer marketing dynamics.SAGraph uniquely integrates user profiles, content features, and temporal interaction patterns, enabling in-depth analysis of influencer marketing mechanisms.Experimental results using both traditional baselines and state-of-the-art large language models (LLMs) demonstrate the crucial role of content analysis in predicting advertising effectiveness.Our findings reveal that LLM-based approaches achieve superior performance in understanding and predicting campaign success, opening new avenues for data-driven influencer marketing strategies.We hope that this dataset will inspire further research: https

In permissioned blockchains, the bandwidth of consensus nodes is mainly consumed by transaction ordering and block distribution; hence, the allocation of consensus nodes' bandwidth makes a significant difference to the system throughput. Previous research focuses on the consensus layer and attempts to optimize consensus protocols to improve throughput, which, however, neglects the impact of data distribution on the throughput and transfers performance bottlenecks to the network layer. In fact, the overall throughput of permissioned blockchains is co-determined by data production in the consensus layer and data distribution in the network layer. This paper proposes a novel data flow framework composed of Predis and Multi-Zone. The former is a data production strategy for permissioned blockchains that employ leader-based BFT protocols and the latter, its corresponding network topology. Predis enables each consensus node to contribute its idle bandwidth for block content pre-distribution so that a much higher volume of transactions can be confirmed in one consensus round, significantly increasing consensus efficiency. Multi-Zone is a network topology to distribute blocks. It can regulate the bandwidth consumption of consensus nodes at a certain value during data distribution and effectively reduce block propagation latency. To test our framework, we implement Predis based on Hotstuff and PBFT, respectively, and experiments show that Predis significantly improves their throughput by 300% to 800%. Multi-Zone is implemented on BFT-SMaRt and compared with random and star network topologies, and it is shown that Multi-Zone holds excellent scalability and the capability of reducing block propagation latency by at least 50%.

Block capacity plays a critical role in maintaining blockchain security and improving transactions per second (TPS). Increasing block capacity can help attain higher TPS, but it also prolongs block propagation latency and degrades system security. In the present paper, we argue that existing work compressing the block size to shorten block propagation latency introduces an undesired side effect, which is that the size of compressed blocks will increase with transaction volume. Instead, we propose Dino, a new block transmission protocol between two peers. Once a node receives a Dino block, it can recover the original block with that Dino block and transactions in its transaction pool. Since Dino transmits block construction rules instead of compressed block content, it has good scalability to transmit blocks with larger transaction volume. We deploy Dino into Bitcoin and Bitcoin Cash to compare it with the state-of-art protocols: Compact, XThin, and Graphene. For a block with 3,000 transactions, its corresponding Dino block is no more than 1 KB in size, which is only 4% of a XThin block, 5% of a Compact block, and 20% of a Graphene block. The size of Dino blocks stays constant when the transaction volume reaches Bitcoin and Bitcoin Cash’s protocol limit. Simulation experiments show that Dino scales well with higher transaction generation rates and can reduce block propagation latency.