A$k$-clique is a dense graph, consisting of$k$fully-connected nodes, that finds numerous applications, such as community detection and network analysis. In this paper, we study a new problem, that finds a maximum set of disjoint$k$-cliques in a given large real-world graph with a user-defined fixed number$k$, which can contribute to a good performance of teaming collaborative events in online games. However, this problem is NP-hard when$k\geq 3$, making it difficult to solve. To address that, we propose an efficient lightweight method that avoids significant overheads and achieves a$k$-approximation to the optimal, which is equipped with several optimization techniques, including the ordering method, degree estimation in the clique graph, and a lightweight implementation. Besides, to handle dynamic graphs that are widely seen in real-world social networks, we devise an efficient indexing method with careful swapping operations, leading to the efficient maintenance of a near-optimal result with frequent updates in the graph. In various experiments on several large graphs, our proposed approaches significantly outperform the competitors by up to 2 orders of magnitude in running time and 13.3% in the number of computed disjoint$k$-cliques, which demonstrates the superiority of the proposed approaches in terms of efficiency and effectiveness.

Multimodal interfaces go beyond the traditional interaction through keyboard and mouse by incorporating multiple modes of interaction, such as touch, voice, gesture, and even gaze, to create more intuitive and immersive user experiences. This paper investigates how TinyML can be employed for multimodal interfaces in the context of games. An endless game in which the character has to avoid obstacles and fight monsters to advance has been developed. An Arduino Nano 33 BLE Sense is then used as the input device for the game by recognizing the hand gestures of the players.

In this paper, for the first time, we introduce the concepts of window-CCs and window-SCCs on undirected and directed temporal graphs, respectively. We then study the queries of window-CC and window-SCC by developing several efficient index-based query solutions. The space costs of the best indices are linear to the sizes of the temporal graphs. The extensive experimental evaluation on 12 real-world datasets demonstrates the high efficiency and effectiveness of the proposed solutions. In the future, we will develop distributed index construction algorithms, which would be useful for very large temporal graphs containing billions of edges. In the future, we will implement our algorithms by using a distributed computing platform (e.g., Pregel), which would be very useful when the temporal graph is too large to be kept by a single machine.