Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics

The operation of creating edges has been widely applied to optimize relevant quantities of opinion dynamics. In this paper, we consider a problem of polarization optimization for the leader-follower opinion dynamics in a noisy social network with $n$ nodes and $m$ edges, where a group $Q$ of $q$ nodes are leaders, and the remaining $n-q$ nodes are followers. We adopt the popular leader-follower DeGroot model, where the opinion of every leader is identical and remains unchanged, while the opinion of every follower is subject to white noise. The polarization is defined as the steady-state variance of the deviation of each node's opinion from leaders' opinion, which equals one half of the effective resistance $\mathcal{R}_Q$ between the node group $Q$ and all other nodes. Concretely, we propose and study the problem of minimizing $\mathcal{R}_Q$ by adding $k$ new edges with each incident to a node in $Q$. We show that the objective function is monotone and supermodular. We then propose a simple greedy algorithm with an approximation factor $1-1/e$ that approximately solves the problem in $O((n-q)^3)$ time. To speed up the computation, we also provide a fast algorithm to compute (1-1/e-\eps)-approximate effective resistance $\mathcal{R}_Q$, the running time of which is \Otil (mk\eps^{-2}) for any \eps>0, where the \Otil (\cdot) notation suppresses the ${\rm poly} (\log n)$ factors. Extensive experiment results show that our second algorithm is both effective and efficient.Comment: This paper has been accepted in CIKM'23 conferenc

Optimal Scale-Free Small-World Graphs with Minimum Scaling of Cover Time

The cover time of random walks on a graph has found wide practical applications in different fields of computer science, such as crawling and searching on the World Wide Web and query processing in sensor networks, with the application effects dependent on the behavior of cover time: the smaller the cover time, the better the application performance. It was proved that over all graphs with $N$ nodes, complete graphs have the minimum cover time $N\log N$. However, complete graphs cannot mimic real-world networks with small average degree and scale-free small-world properties, for which the cover time has not been examined carefully, and its behavior is still not well understood. In this paper, we first experimentally evaluate the cover time for various real-world networks with scale-free small-world properties, which scales as $N\log N$. To better understand the behavior of the cover time for real-world networks, we then study the cover time of three scale-free small-world model networks by using the connection between cover time and resistance diameter. For all the three networks, their cover time also behaves as $N\log N$. This work indicates that sparse networks with scale-free and small-world topology are favorable architectures with optimal scaling of cover time. Our results deepen understanding the behavior of cover time in real-world networks with scale-free small-world structure, and have potential implications in the design of efficient algorithms related to cover time

Facilitating dynamic web service composition with fine-granularity context management

Context is an important factor for the success of dynamic service composition. Although many contextbased AI or workflow approaches have been proposed to support dynamic service composition, there is still an unaddressed issue of the support of fine-granularity context management. In this paper, we propose a granularity-based context model together with an approach to supporting the intelligent context-aware service composing problem. The corresponding case study is provided to show the validity of our approach.<br /

Intelligent-Unrolling: Exploiting Regular Patterns in Irregular Applications

Modern optimizing compilers are able to exploit memory access or computation patterns to generate vectorization codes. However, such patterns in irregular applications are unknown until runtime due to the input dependence. Thus, either compiler's static optimization or profile-guided optimization based on specific inputs cannot predict the patterns for any common input, which leads to suboptimal code generation. To address this challenge, we develop Intelligent-Unroll, a framework to automatically optimize irregular applications with vectorization. Intelligent-Unroll allows the users to depict the computation task using \textit{code seed} with the memory access and computation patterns represented in \textit{feature table} and \textit{information-code tree}, and generates highly efficient codes. Furthermore, Intelligent-Unroll employs several novel optimization techniques to optimize reduction operations and gather/scatter instructions. We evaluate Intelligent-Unroll with sparse matrix-vector multiplication (SpMV) and graph applications. Experimental results show that Intelligent-Unroll is able to generate more efficient vectorization codes compared to the state-of-the-art implementations

From regular to growing small-world networks

We propose a growing model which interpolates between one-dimensional regular lattice and small-world networks. The model undergoes an interesting phase transition from large to small world. We investigate the structural properties by both theoretical predictions and numerical simulations. Our growing model is a complementarity for the famous static WS network model.Comment: 5 pages, 4 figure

Deterministic weighted scale-free small-world networks

We propose a deterministic weighted scale-free small-world model for considering pseudofractal web with the coevolution of topology and weight. In the model, we have the degree distribution exponent $\gamma$ restricted to a range between 2 and 3, simultaneously tunable with two parameters. At the same time, we provide a relatively complete view of topological structure and weight dynamics characteristics of the networks: weight and strength distribution; degree correlations; average clustering coefficient and degree-cluster correlations; as well as the diameter. We show that our model is particularly effective at mimicing weighted scale-free small-world networks with a high and relatively stable clustering coefficient, which rapidly decline with the network size in most previous models.Comment: a paper with 15 pages and 5 figure