70 research outputs found
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 nodes and edges, where a group of nodes are
leaders, and the remaining 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 between the node group and all
other nodes. Concretely, we propose and study the problem of minimizing
by adding new edges with each incident to a node in . We
show that the objective function is monotone and supermodular. We then propose
a simple greedy algorithm with an approximation factor that
approximately solves the problem in time. To speed up the
computation, we also provide a fast algorithm to compute
(1-1/e-\eps)-approximate effective resistance , the running
time of which is \Otil (mk\eps^{-2}) for any \eps>0, where the \Otil
(\cdot) notation suppresses the 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 nodes, complete graphs have the minimum cover time . 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
. 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 . 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 /
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
Despite the great success of large language models (LLMs) in various tasks,
they suffer from generating hallucinations. We introduce Truth Forest, a method
that enhances truthfulness in LLMs by uncovering hidden truth representations
using multi-dimensional orthogonal probes. Specifically, it creates multiple
orthogonal bases for modeling truth by incorporating orthogonal constraints
into the probes. Moreover, we introduce Random Peek, a systematic technique
considering an extended range of positions within the sequence, reducing the
gap between discerning and generating truth features in LLMs. By employing this
approach, we improved the truthfulness of Llama-2-7B from 40.8\% to 74.5\% on
TruthfulQA. Likewise, significant improvements are observed in fine-tuned
models. We conducted a thorough analysis of truth features using probes. Our
visualization results show that orthogonal probes capture complementary
truth-related features, forming well-defined clusters that reveal the inherent
structure of the dataset.Comment: Accepted as AAAI 202
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
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