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Guanfeng Liu

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23 papers

2 author rows

AAAI Conference 2026 Conference Paper

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

Huayang Xu
Huanhuan Yuan
Guanfeng Liu
Junhua Fang
Lei Zhao
Pengpeng Zhao

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users’ historical interaction data. Given that users’ complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation (WEARec). Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work synergistically to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of WEARec.

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IJCAI Conference 2025 Conference Paper

Adaptive Graph Unlearning

Pengfei Ding
Yan Wang
Guanfeng Liu
Jiajie Zhu

Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contain outdated, inaccurate, or privacy-sensitive information. However, existing methods often suffer from (1) incomplete or over unlearning due to neglecting the distinct objectives of different unlearning tasks, and (2) inaccurate identification of neighbors affected by deleted elements across various GNN architectures. To address these limitations, we propose AGU, a novel Adaptive Graph Unlearning framework that flexibly adapts to diverse unlearning tasks and GNN architectures. AGU ensures the complete forgetting of deleted elements while preserving the integrity of the remaining graph. It also accurately identifies affected neighbors for each GNN architecture and prioritizes important ones to enhance unlearning performance. Extensive experiments on seven real-world graphs demonstrate that AGU outperforms existing methods in terms of effectiveness, efficiency, and unlearning capability.

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AAAI Conference 2025 Conference Paper

Fuzzy Collaborative Reasoning

Huanhuan Yuan
Pengpeng Zhao
Jiaqing Fan
Junhua Fang
Guanfeng Liu
Victor S. Sheng

Collaborative reasoning enhances recommendation performance by combining the strengths of symbolic learning and deep neural learning. However, current collaborative reasoning models rely on parameterized networks to simulate logical operations within the reasoning process, which (1) do not comply with all axiomatic principles of classical logic and (2) limit the model's generalizability. To address these limitations, a Fuzzy logic approach tailored for Collaborative Reasoning (FuzzCR) is proposed in this work, aiming to augment the recommendation system with cognitive abilities. Specifically, this method redefines the sequential recommendation task as a logical query answering process to facilitate a more structured and logical progression of reasoning. Moreover, learning-free fuzzy logical operations are implemented for the designed reasoning process. Taking advantage of the inherent properties of fuzzy logic, these logical operations satisfy fundamental logical rules and ensure complete reasoning. After training, these operations can be applied to flexible reasoning processes, rather than being confined to fixed computation graphs, thereby exhibiting good generalizability. Extensive experiments conducted on publicly available datasets demonstrate the superiority of this method in solving the sequential recommendation task.

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IJCAI Conference 2025 Conference Paper

GPL4SRec: Graph Multi-Level Aware Prompt Learning for Streaming Recommendation

Hao Cang
Huanhuan Yuan
Jiaqing Fan
Lei Zhao
Guanfeng Liu
Pengpeng Zhao

Streaming Recommendation (SRec) aims to capture evolving user preferences in the streaming scenarios. Recently, Graph Prompt Learning (GPL) methods have demonstrated their effectiveness and adaptability within SRec. However, existing graph prompt solutions rarely consider the evolution of multi-hop cascading relationships between users and items, which are crucial for modeling the shifts in user preferences. To address this problem, we propose a novel Graph Multi-Level Aware Prompt Learning for Streaming Recommendation, named GPL4SRec. Specifically, a graph encoder is first pre-trained on extensive historical data to capture user long-term preferences. Then, we design three types of prompts, namely node-aware, structure-aware, and layer-aware prompts, which are used to guide the pre-trained encoder to better capture user short-term preferences. This is accomplished by accounting for both the incremental changes in users and items, as well as the cascading evolution in multi-hop relationships. Furthermore, we provide a theoretical analysis showing that our prompt templates are critical to achieving superior performance. Finally, experimental results also prove that our model significantly outperforms the state-of-the-art approaches in SRec.

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TAAS Journal 2025 Journal Article

Privacy-Preserving Group-by-Aggregation Queries for Data Federation under V2X environment

Zicheng Cao
Guanfeng Liu
Qingzhi Ma
Wei Chen
Lei Zhao
An Liu

Vehicle-to-everything (V2X) technology enables vehicles to communicate with each other, infrastructure, and the cloud, facilitating intelligent traffic management and vehicle interconnection. However, the data generated by vehicles raises concerns regarding personal privacy and corporate interests. With the rapid development of V2X technology, data security issues are becoming increasingly prominent. Data federation, as an emerging data-sharing model, utilizes secure multi-party computation techniques to enable collaboration among data owners without disclosing raw data, offering a new approach to addressing privacy and security concerns in the data exchange process of V2X. This paper proposes a group-by-aggregation query algorithm for data federation, aiming to protect personal privacy data while facilitating effective data sharing and analysis. The algorithm reverses the traditional group-by-aggregation queries process by not transmitting grouping results but rather passing encrypted aggregated attribute values to relevant data owners. By leveraging encryption algorithms with additive homomorphic or order-preserving properties to encrypt the aggregated attribute values, the algorithm ensures the correctness of mathematical operations performed under encryption, such as addition and comparison operations. Finally, the effectiveness and practicality of the algorithm are validated through experimental evaluations.

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IJCAI Conference 2025 Conference Paper

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks

Han Zhang
Yan Wang
Guanfeng Liu
Pengfei Ding
Huaxiong Wang
Kwok-Yan Lam

To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has emerged. However, two major limitations severely degrade the performance and hinder the generalizability of existing XGNN methods: they (a) fail to capture the complete decision logic of GNNs across diverse distributions in the entire dataset's sample space, and (b) impose strict prerequisites on edge properties and GNN internal accessibility. To address these limitations, we propose OPEN, a novel cOmprehensive and Prerequisite-free Explainer for GNNs. OPEN, as the first work in the literature, can infer and partition the entire dataset's sample space into multiple environments, each containing graphs that follow a distinct distribution. OPEN further learns the decision logic of GNNs across different distributions by sampling subgraphs from each environment and analyzing their predictions, thus eliminating the need for strict prerequisites. Experimental results demonstrate that OPEN captures nearly complete decision logic of GNNs, outperforms state-of-the-art methods in fidelity while maintaining similar efficiency, and enhances robustness in real-world scenarios.

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IJCAI Conference 2023 Conference Paper

Sequential Recommendation with Probabilistic Logical Reasoning

Huanhuan Yuan
Pengpeng Zhao
Xuefeng Xian
Guanfeng Liu
Yanchi Liu
Victor S. Sheng
Lei Zhao

Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR to be equipped with concurrent perception and cognition capacities. However, neural-symbolic SR remains a challenging problem due to open issues like representing users and items in logical reasoning. In this paper, we combine the Deep Neural Network (DNN) SR models with logical reasoning and propose a general framework named Sequential Recommendation with Probabilistic Logical Reasoning (short for SR-PLR). This framework allows SR-PLR to benefit from both similarity matching and logical reasoning by disentangling feature embedding and logic embedding in the DNN and probabilistic logic network. To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns. Then the feature and logic representations learned from the DNN and logic network are concatenated to make the prediction. Finally, experiments on various sequential recommendation models demonstrate the effectiveness of the SR-PLR. Our code is available at https: //github. com/Huanhuaneryuan/SR-PLR.

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TIST Journal 2022 Journal Article

Supply-Demand-aware Deep Reinforcement Learning for Dynamic Fleet Management

Bolong Zheng
Lingfeng Ming
Qi Hu
Zhipeng Lü
Guanfeng Liu
Xiaofang Zhou

Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are idle and that passengers spend on waiting. As a key component of these platforms, the fleet management problem can be naturally modeled as a Markov Decision Process, which enables us to use the deep reinforcement learning. However, existing studies are proposed based on simplified problem settings that fail to model the complicated supply-dynamics and restrict the performance in the real traffic environment. In this article, we propose a supply-demand-aware deep reinforcement learning algorithm for taxi dispatching, where we use a deep Q-network with action sampling policy, called AS-DQN, to learn an optimal dispatching policy. Furthermore, we utilize a dueling network architecture, called AS-DDQN, to improve the performance of AS-DQN. Extensive experiments on real-world datasets offer insight into the performance of our model and show that it is capable of outperforming the baseline approaches.

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IJCAI Conference 2021 Conference Paper

Cross-Domain Recommendation: Challenges, Progress, and Prospects

Feng Zhu
Yan Wang
Chaochao Chen
Jun Zhou
Longfei Li
Guanfeng Liu

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising prospects in CDR.

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IJCAI Conference 2020 Conference Paper

A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

Feng Zhu
Yan Wang
Chaochao Chen
Guanfeng Liu
Xiaolin Zheng

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.

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IS Journal 2020 Journal Article

Personalized Geographical Influence Modeling for POI Recommendation

Yanan Zhang
Guanfeng Liu
An Liu
Yifan Zhang
Zhixu Li
Xiangliang Zhang
Qing Li

Point-of-interest (POI) recommendation has great significance in helping users find favorite places from a large number of candidate venues. One challenging in POI recommendation is to effectively exploit geographical information since users usually care about the physical distance to the recommended POIs. Though spatial relevance has been widely considered in recent recommendation methods, it is modeled only from the POI perspective, failing to capture user personalized preference to spatial distance. Moreover, these methods suffer from a diversity-deficiency problem since they are often based on collaborative filtering which always favors popular POIs. To overcome these problems, we propose in this article a personalized geographical influence modeling method called PGIM, which jointly learns users’ geographical preference and diversity preference for POI recommendation. Specifically, we model geographical preference from three aspects: user global tolerance, user local tolerance, and spatial distance. We also extract user diversity preference from interactions among users for diversity-promoting recommendation. Experimental results on three real-world datasets demonstrate the superiority of PGIM.

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IJCAI Conference 2019 Conference Paper

DMRAN: A Hierarchical Fine-Grained Attention-Based Network for Recommendation

Huizhao Wang
Guanfeng Liu
An Liu
Zhixu Li
Kai Zheng

The conventional methods for the next-item recommendation are generally based on RNN or one- dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i. e. , Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Feature-level Deeper Self-Attention Network for Sequential Recommendation

Tingting Zhang
Pengpeng Zhao
Yanchi Liu
Victor S. Sheng
Jiajie Xu
Deqing Wang
Guanfeng Liu
Xiaofang Zhou

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation.

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AAAI Conference 2019 Conference Paper

Preference-Aware Task Assignment in Spatial Crowdsourcing

Yan Zhao
Jinfu Xia
Guanfeng Liu
Han Su
Defu Lian
Shuo Shang
Kai Zheng

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

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IJCAI Conference 2019 Conference Paper

Profit-driven Task Assignment in Spatial Crowdsourcing

Jinfu Xia
Yan Zhao
Guanfeng Liu
Jiajie Xu
Min Zhang
Kai Zheng

In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i. e. , expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.

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IJCAI Conference 2018 Conference Paper

A Deep Framework for Cross-Domain and Cross-System Recommendations

Feng Zhu
Yan Wang
Chaochao Chen
Guanfeng Liu
Mehmet Orgun
Jia Wu

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e. g. , ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

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AAAI Conference 2014 Conference Paper

Trust Prediction with Propagation and Similarity Regularization

Xiaoming Zheng
Yan Wang
Mehmet Orgun
Youliang Zhong
Guanfeng Liu

Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decisionmaking of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendencyreduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendencyreduced values of missing trust ratings, incorporating both propagated trust and the similarity of users’ rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a realworld dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.

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AAAI Conference 2012 Conference Paper

Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks

Guanfeng Liu
Yan Wang
Mehmet Orgun

Trust is one of the most important factors for participants’ decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences on both social interaction between participants and trust evaluation. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Social Context-Aware trust Network discovery algorithm (SCAN) by adopting the Monte Carlo method and our proposed optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust network.

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ICRA Conference 2011 Conference Paper

A practical algorithm for robot path planning with imprecise environmental dimensions

Guanfeng Liu

Robot path planning problem arises in a variety of industrial application scenarios. In manufacturing automation, we often require a robot to quickly go to its home position from a given initial point while avoiding possible collisions with the environment. In hard disk drive testing, fast recovery following from a robot failure, for example, a motion stop or robot gripper sensor failure, is desired in order to reduce manual intervening and improve production efficiency. In the academic field, under the assumption that the environment dimension is completely known, the robot path planning problem has been solved theoretically using either the complete methods, or the probabilistic algorithms. However, in industrial fields, most commonly used path planning algorithms are still empirical, and are often based on human intuition. There is still a big gap between the state-of-the-art path planning techniques that are already developed in the academic field and the methods used in the industrial fields. This paper reports a practical approach for path planning with an intention to bridge the gap. This approach constructs a set of collision-free milestones iteratively, which gives rise to a partition of robot configuration space such that the collision-free portion of each cell is star-shaped. Then it is straightforward to draw a collision-free path from a point in robot configuration space to one of the collision-free milestones as well as constructing and verifying paths among these milestones.

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AAAI Conference 2011 Conference Paper

Trust Transitivity in Complex Social Networks

Guanfeng Liu
Yan Wang
Mehmet Orgun

In Online Social Networks (OSNs), participants can conduct rich activities, where trust is one of the most important factors for their decision making. This necessitates the evaluation of the trustworthiness between two unknown participants along the social trust paths between them based on the trust transitivity properties (i. e. , if A trusts B and B trusts C, then A can trust C to some extent). In order to compute more reasonable trust value between two unknown participants, a critical and challenging problem is to make clear how and to what extent trust is transitive along a social trust path. To address this problem, we ﬁrst propose a new complex social network structure that takes, besides trust, social relationships, recommendation roles and preference similarity between participants into account. These factors have signiﬁcant inﬂuence on trust transitivity. We then propose a general concept, called Quality of Trust Transitivity (QoTT), that takes any factor with impact on trust transitivity as an attribute to illustrate the ability of a trust path to guarantee a certain level of quality in trust transitivity. Finally, we propose a novel Multiple QoTT Constrained Trust Transitivity (MQCTT) model. The results of our experiments demonstrate that our proposed MQCTT model follows the properties of trust and the principles illustrated in social psychology, and thus can compute more resonable trust values than existing methods that consider neither the impact of social aspects nor the properties of trust.

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AAAI Conference 2010 Conference Paper

Optimal Social Trust Path Selection in Complex Social Networks

Guanfeng Liu
Yan Wang
Mehmet Orgun

Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trustworthiness between two unknown participants along a certain social trust path between them in the social network. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.

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AAMAS Conference 2010 Conference Paper

Quality of Trust for Social Trust Path Selection in Complex Social Networks

Guanfeng Liu
Yan Wang
Mehmet Orgun

In online social networks, there are usually many social trust paths between agents. Thus, a challenging problem is which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we present a new complex social network structure and propose a new concept, Quality of Trust (QoT), for social trust path selection in complex social networks.

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ICRA Conference 2002 Conference Paper

Topology Analysis of Close-Chain Mechanisms

Yulie Wu
Xuezhong Wu
Guanfeng Liu
Shengyi Li

Using standard idea from differential topology, this paper presents a detailed analysis of the topology structure of configuration space of general close-chain mechanisms. A diffeomorphic theorem on configuration space is proposed and its proof is given by using the Morse theory and critical value theory. Relations between the configuration space singularity and topology structure are described, which show that the topology structure of configuration space of close-chain mechanisms will change at the configuration singularity. Finally, the topology structure of a four-bar planar mechanism is analyzed, as an example, to verify the proposed theorem.

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