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Xiang Cheng

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24 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Calibrated LRT Guidance for Offline Diffusion Policies

Ximan Sun
Xiang Cheng

Diffusion policies are competitive for offline Reinforcement Learning but are typically guided at sampling time by heuristics that lack a statistical notion of risk. We introduce LRT-Diffusion, a risk-aware sampling rule that performs evidence accumulation between two inference-time heads: an unconditional background head and a state-conditional good head. Concretely, we accumulate a log-likelihood ratio and gate the conditional mean with a logistic controller whose threshold𝜏 is calibrated once per task and per sampler under 𝐻0 to meet a user-specified Type-I level 𝛼. This turns guidance from a fixed push into an evidence-driven adjustment with a user-interpretable risk budget. Importantly, we deliberately leave training vanilla (two heads with standard 𝜖-prediction) under the structure of DDPM. LRT guidance composes naturally with Q-gradients: critic-gradient updates can be taken at the unconditional mean, at the LRT-gated mean, or a blend, exposing a continuum from exploitation to conservatism. We standardize states/actions consistently at train and test time and report a stateconditional OOD metric alongside return. On D4RL MuJoCo tasks, LRT-Diffusion yields a calibrated return–risk frontier: LRT often reduces state-conditional OOD, and combining with a small Q-step increases return along the frontier. Theoretically, we establish level- 𝛼 calibration, stability bounds, and a return comparison showing when evidence-gated guidance is preferable to pure Q-guidance. Overall, LRT-Diffusion is a drop-in, inference-time method that adds principled, calibrated risk control to diffusion policies for offline RL.

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AAAI Conference 2026 Conference Paper

From Chaos to Cure: A Prefix Heuristics Guided Model-Agnostic Adaptive Detoxification Framework

Yuhu Shang
Xiang Cheng
Yimeng Ren
Huijia Wu
Xuexiong Luo
Kangkang Lu
Jian Zhao
Zhaofeng He

The impressive performance of large language models (LLMs) also brings inherent toxicity risks, prompting the need for effective detoxification to support responsible deployment. Prevailing methods generally follow an inflexible model-specific fashion, addressing only individual models or model families. Moreover, overlooking the underlying toxic risks involved in the input prefix can lead to toxic accumulation during autoregressive generation. Existing methods rely on external strong attribute interventions to address this issue, which further exacerbates contextual semantic inconsistencies and makes it difficult to balance toxicity efficacy and generation quality. To address these concerns, we propose a novel Model-Agnostic Adaptive Detoxification (MAAD) framework. To address accumulating toxicity, we present prefix heuristics that serve as contextual signals, guiding the base LLM toward safer generation. Along this line, we construct an antidote dataset to support a lightweight model, Detoxifier, which steers the base LLM to make in-scope and reliable detoxifying distribution adjustments while preserving fluency and contextual understanding. Designed as an easy-to-deploy module, Detoxifier requires a small amount of data and can be seamlessly applied to various base LLMs with one-off training. Since over-purifying often reduces diversity, we also propose a dynamic truncation method called CW-cutoff sampling to trade off language model quality and diversity. Extensive experiments demonstrate that MAAD strikes a better balance between detoxification effectiveness and generation quality, while also maintaining model utility.

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AAAI Conference 2026 Conference Paper

PrivSV: Differentially Private Steering Vector for Large Language Models

Haocheng Yang
Xiang Cheng
Chenhao Sun
Pengfei Zhang
Sen Su

Steering Vector (SV) is a powerful technique for controlling Large Language Models (LLMs) by manipulating their activations without altering model weights. However, when constructed from sensitive data, SV poses significant privacy risks, as it may leak private information. Existing differential privacy (DP) techniques for constructing SV cannot be directly applied to training-based SV construction paradigms, which offer higher task performance. In this work, we present **PrivSV**, a general privacy-preserving approach for constructing SV with DP guarantees, compatible with arbitrary SV construction paradigms while maintaining high utility. In PrivSV, we propose three novel methods: a Layer-wise Noise-Resilient Reduction (LNR²) method to reduce the injected noise in high-dimensional SV; a Directional Prior Compensation (DPC) method to recover utility degraded by noise perturbation; and a Privacy-Aware Optimal Parameter Determination (POPD) method to adaptively maximize the performance of the final compensated SV. Extensive experiments on open-source LLMs of different families (i.e., LlaMa, Qwen, Mistral and Gemma) demonstrate that PrivSV outperforms several existing techniques across various privacy budgets.

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AAAI Conference 2026 Conference Paper

TinyChemVL: Advancing Chemical Vision-Language Models via Efficient Visual Token Reduction and Complex Reaction Tasks

Xuanle Zhao
Shuxin Zeng
Xinyuan Cai
Xiang Cheng
Duzhen Zhang
Xiuyi Chen
Bo Xu

While Vision Language Models (VLMs) have demonstrated remarkable capabilities in general visual understanding, their application in the chemical domain has been limited, with previous works predominantly focusing on text and thus overlooking critical visual information, such as molecular structures. Current approaches that directly adopt standard VLMs for chemical tasks suffer from two primary issues: (i) computational inefficiency of processing entire chemical images with non-informative backgrounds. (ii) a narrow scope on molecular-level tasks that restricts progress in chemical reasoning. In this work, we propose TinyChemVL, an efficient and powerful chemical VLM that leverages visual token reduction and reaction-level tasks to improve model efficiency and reasoning capacity. Also, we propose ChemRxn-V, a reaction-level benchmark for assessing vision-based reaction recognition and prediction tasks. Directly predicting reaction products from molecular images poses a non-trivial challenge, as it requires models to integrate both recognition and reasoning capacities. Our results demonstrate that, with only 4B parameters, TinyChemVL achieves superior performance on both molecular and reaction tasks, while also demonstrating faster inference and training speeds compared to existing models. Notably, TinyChemVL outperforms ChemVLM while utilizing only 1/16th of the visual tokens. This work builds efficient yet powerful VLMs for chemical domains by co-designing model architecture and task complexity.

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AAAI Conference 2026 Conference Paper

Towards Robust Event-Based Depth Estimation: Bridging Synthetic and Real Domains with Motion Adaptation

Yuzhe Ji
Haotian Wang
Yijie Chen
Xiang Cheng
Liuqing Yang
Xinhu Zheng

Event cameras provide microsecond latency and high dynamic range, making them ideal for 3D perception tasks in traffic scenes with challenging lighting conditions. Yet existing methods often struggle to generalize to out-of-domain environments due to the limited availability of diverse training data. While synthetic data offers an easily accessible alternative, it introduces a significant sim-to-real gap, particularly in motion patterns. We tackle this challenge by introducing Motion-Adaptation Mamba (MA-Mamba), a dual-track framework that advances both architecture and data augmentation. At the architectural level, we introduce a lightweight Spatio-Temporal Association module that captures motion-induced appearance variations at arbitrary scales, and an Adaptive Memory Balancing module, built on the Mamba state-space framework, that adaptively filters memory updates to maintain stable scene context under diverse dynamics. At the data level, we design event-oriented augmentations that simulate varied motion patterns and apply priority-based masked sequence modeling to strengthen long-range spatio-temporal reasoning. Trained solely on synthetic data, MA-Mamba delivers substantial zero-shot gains on multiple real-world benchmarks, demonstrating strong robustness and generalizability.

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NeurIPS Conference 2025 Conference Paper

From Softmax to Score: Transformers Can Effectively Implement In-Context Denoising Steps

Paul Rosu
Lawrence Carin
Xiang Cheng

Transformers have emerged as powerful meta-learners, with growing evidence that they implement learning algorithms within their forward pass. We study this phenomenon in the context of denoising, presenting a unified framework that shows Transformers can implement (a) manifold denoising via Laplacian flows, (b) score-based denoising from diffusion models, and (c) a generalized form of anisotropic diffusion denoising. Our theory establishes exact equivalence between Transformer attention updates and these algorithms. Empirically, we validate these findings on image denoising tasks, showing that even simple Transformers can perform robust denoising both with and without context. These results illustrate the Transformer’s flexibility as a denoising meta-learner. Code available at https: //github. com/paulrosu11/Transformers are Diffusion_Denoisers.

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ICLR Conference 2025 Conference Paper

Graph Transformers Dream of Electric Flow

Xiang Cheng
Lawrence Carin
Suvrit Sra

We show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eigenvector decomposition. The Transformer has access to information on the input graph only via the graph's incidence matrix. We present explicit weight configurations for implementing each algorithm, and we bound the constructed Transformers' errors by the errors of the underlying algorithms. Our theoretical findings are corroborated by experiments on synthetic data. Additionally, on a real-world molecular regression task, we observe that the linear Transformer is capable of learning a more effective positional encoding than the default one based on Laplacian eigenvectors. Our work is an initial step towards elucidating the inner-workings of the Transformer for graph data. Code is available at https://github.com/chengxiang/LinearGraphTransformer

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ICML Conference 2025 Conference Paper

On Understanding Attention-Based In-Context Learning for Categorical Data

Aaron T. Wang
William Convertino
Xiang Cheng
Ricardo Henao
Lawrence Carin

In-context learning based on attention models is examined for data with categorical outcomes, with inference in such models viewed from the perspective of functional gradient descent (GD). We develop a network composed of attention blocks, with each block employing a self-attention layer followed by a cross-attention layer, with associated skip connections. This model can exactly perform multi-step functional GD inference for in-context inference with categorical observations. We perform a theoretical analysis of this setup, generalizing many prior assumptions in this line of work, including the class of attention mechanisms for which it is appropriate. We demonstrate the framework empirically on synthetic data, image classification and language generation.

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ICLR Conference 2024 Conference Paper

Linear attention is (maybe) all you need (to understand Transformer optimization)

Kwangjun Ahn
Xiang Cheng
Minhak Song
Chulhee Yun
Ali Jadbabaie
Suvrit Sra

Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics. We make progress towards understanding the subtleties of training Transformers by carefully studying a simple yet canonical linearized *shallow* Transformer model. Specifically, we train linear Transformers to solve regression tasks, inspired by J. von Oswald et al. (ICML 2023), and K. Ahn et al. (NeurIPS 2023). Most importantly, we observe that our proposed linearized models can reproduce several prominent aspects of Transformer training dynamics. Consequently, the results obtained in this paper suggest that a simple linearized Transformer model could actually be a valuable, realistic abstraction for understanding Transformer optimization.

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ICML Conference 2024 Conference Paper

Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context

Xiang Cheng
Yuxin Chen
Suvrit Sra

Many neural network architectures are known to be Turing Complete, and can thus, in principle implement arbitrary algorithms. However, Transformers are unique in that they can implement gradient-based learning algorithms under simple parameter configurations. This paper provides theoretical and empirical evidence that (non-linear) Transformers naturally learn to implement gradient descent in function space, which in turn enable them to learn non-linear functions in context. Our results apply to a broad class of combinations of non-linear architectures and non-linear in-context learning tasks. Additionally, we show that the optimal choice of non-linear activation depends in a natural way on the class of functions that need to be learned.

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NeurIPS Conference 2023 Conference Paper

Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions

Xiang Cheng
Bohan Wang
Jingzhao Zhang
Yusong Zhu

MCMC algorithms offer empirically efficient tools for sampling from a target distribution $\pi(x) \propto \exp(-V(x))$. However, on the theory side, MCMC algorithms suffer from slow mixing rate when $\pi(x)$ is non-log-concave. Our work examines this gap and shows that when Poincar\'e-style inequality holds on a subset $\mathcal{X}$ of the state space, the conditional distribution of MCMC iterates over $\mathcal{X}$ mixes fast to the true conditional distribution. This fast mixing guarantee can hold in cases when global mixing is provably slow. We formalize the statement and quantify the conditional mixing rate. We further show that conditional mixing can have interesting implications for sampling from mixtures of Gaussians, parameter estimation for Gaussian mixture models, and Gibbs-sampling with well-connected local minima.

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NeurIPS Conference 2023 Conference Paper

Restart Sampling for Improving Generative Processes

Yilun Xu
Mingyang Deng
Xiang Cheng
Yonglong Tian
Ziming Liu
Tommi Jaakkola

Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plateau in performance while SDE-based samplers deliver higher sample quality at the cost of increased sampling time. We attribute this difference to sampling errors: ODE-samplers involve smaller discretization errors while stochasticity in SDE contracts accumulated errors. Based on these findings, we propose a novel sampling algorithm called \textit{Restart} in order to better balance discretization errors and contraction. The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE. Empirically, Restart sampler surpasses previous SDE and ODE samplers in both speed and accuracy. Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64{\times} 64$. In addition, it attains significantly better sample quality than ODE samplers within comparable sampling times. Moreover, Restart better balances text-image alignment/visual quality versus diversity than previous samplers in the large-scale text-to-image Stable Diffusion model pre-trained on LAION $512{\times} 512$. Code is available at https: //github. com/Newbeeer/diffusion_restart_sampling

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NeurIPS Conference 2023 Conference Paper

Transformers learn to implement preconditioned gradient descent for in-context learning

Kwangjun Ahn
Xiang Cheng
Hadi Daneshmand
Suvrit Sra

Several recent works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of transformers are expressive enough to simulate iterations of gradient descent. Going beyond the question of expressivity, we ask: \emph{Can transformers learn to implement such algorithms by training over random problem instances? } To our knowledge, we make the first theoretical progress on this question via an analysis of the loss landscape for linear transformers trained over random instances of linear regression. For a single attention layer, we prove the global minimum of the training objective implements a single iteration of preconditioned gradient descent. Notably, the preconditioning matrix not only adapts to the input distribution but also to the variance induced by data inadequacy. For a transformer with $L$ attention layers, we prove certain critical points of the training objective implement $L$ iterations of preconditioned gradient descent. Our results call for future theoretical studies on learning algorithms by training transformers.

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NeurIPS Conference 2022 Conference Paper

Efficient Sampling on Riemannian Manifolds via Langevin MCMC

Xiang Cheng
Jingzhao Zhang
Suvrit Sra

We study the task of efficiently sampling from a Gibbs distribution $d \pi^* = e^{-h} d {\text{vol}}_g$ over a Riemannian manifold $M$ via (geometric) Langevin MCMC; this algorithm involves computing exponential maps in random Gaussian directions and is efficiently implementable in practice. The key to our analysis of Langevin MCMC is a bound on the discretization error of the geometric Euler-Murayama scheme, assuming $\nabla h$ is Lipschitz and $M$ has bounded sectional curvature. Our error bound matches the error of Euclidean Euler-Murayama in terms of its stepsize dependence. Combined with a contraction guarantee for the geometric Langevin Diffusion under Kendall-Cranston coupling, we prove that the Langevin MCMC iterates lie within $\epsilon$-Wasserstein distance of $\pi^*$ after $\tilde{O}(\epsilon^{-2})$ steps, which matches the iteration complexity for Euclidean Langevin MCMC. Our results apply in general settings where $h$ can be nonconvex and $M$ can have negative Ricci curvature. Under additional assumptions that the Riemannian curvature tensor has bounded derivatives, and that $\pi^*$ satisfies a $CD(\cdot, \infty)$ condition, we analyze the stochastic gradient version of Langevin MCMC, and bound its iteration complexity by $\tilde{O}(\epsilon^{-2})$ as well.

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IJCAI Conference 2021 Conference Paper

Differentially Private Correlation Alignment for Domain Adaptation

Kaizhong Jin
Xiang Cheng
Jiaxi Yang
Kaiyuan Shen

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.

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IJCAI Conference 2020 Conference Paper

LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition

Xiang Cheng
Yunzhe Hao
Jiaming Xu
Bo Xu

Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i. \, e. , PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain.

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AAAI Conference 2020 Conference Paper

Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

Qiyu Ren
Xiang Cheng
Sen Su

Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multipassage MRC approaches have shown that employing passages with and without golden answers (i. e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-thenselect framework, where an extractor is ﬁrst used to predict answer candidates, then a selector is used to choose the ﬁnal answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets conﬁrm the effectiveness of our approach.

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IJCAI Conference 2018 Conference Paper

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

Sen Su
Ningning Jia
Xiang Cheng
Shuguang Zhu
Ruiping Li

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.

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IS Journal 2017 Journal Article

5G-Enabled Cooperative Intelligent Vehicular (5GenCIV) Framework: When Benz Meets Marconi

Xiang Cheng
Chen Chen
Wuxiong Zhang
Yang Yang

As one of the most popular social media platforms today, Twitter provides people with an effective way to communicate and interact with each other. Through these interactions, influence among users gradually emerges and changes people's opinions. Although previous work has studied interpersonal influence as the probability of activating others during information diffusion, they ignore an important fact that information diffusion is the result of influence, while dynamic interactions among users produce influence. In this article, the authors propose a novel temporal influence model to learn users' opinion behaviors regarding a specific topic by exploring how influence emerges during communications. The experiments show that their model performs better than other influence models with different influence assumptions when predicting users' future opinions, especially for the users with high opinion diversity.

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IJCAI Conference 2017 Conference Paper

Deep Supervised Hashing with Nonlinear Projections

Sen Su
Gang Chen
Xiang Cheng
Rong Bi

Hashing has attracted broad research interests in large scale image retrieval due to its high search speed and efficient storage. Recently, many deep hashing methods have been proposed to perform simultaneous nonlinear feature learning and hash projection learning, which have shown superior performance compared to hand-crafted feature based hashing methods. Nonlinear projection functions have shown their advantages over the linear ones due to their powerful generalization capabilities. To improve the performance of deep hashing methods by generalizing projection functions, we propose the idea of implementing a pure nonlinear deep hashing network architecture. By consolidating the above idea, this paper presents a Deep Supervised Hashing architecture with Nonlinear Projections (DSHNP). In particular, soft decision trees are adopted as the nonlinear projection functions, since they can generate differentiable nonlinear outputs and can be trained with deep neural networks in an end-to-end way. Moreover, to make the hash codes as independent as possible, we design two regularizers imposed on the parameter matrices of the leaves in the soft decision trees. Extensive evaluations on two benchmark image datasets show that the proposed DSHNP outperforms several state-of-the-art hashing methods.

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IS Journal 2016 Journal Article

Consumer-Centered Energy System for Electric Vehicles and the Smart Grid

Xiang Cheng
Rongqing Zhang
Liuqing Yang

This article proposes the consumer-centered energy system (CCES), a cyber-physical-social system that integrates the power grid (physical), communications and computing (cyber), and consumer interactions (social). The authors introduce some basic features of the CCES, provide a CCES architecture for electric vehicles and smart grid, and propose a request-and-schedule energy management protocol.

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IS Journal 2014 Journal Article

An Energy-Efficient and Swarm Intelligence-Based Routing Protocol for Next-Generation Sensor Networks

Yong Wang
Changle Li
Yulong Duan
Jin Yang
Xiang Cheng

After providing a brief overview of routing protocols for next-generation sensor networks (NGSNs), the authors propose Bee-Sensor-C, an energy-efficient, swarm intelligence-based, and scalable multipath routing protocol that integrates dynamic clustering, multipath routing, and bee-inspired routing to meet the performance requirements of NGSNs. A performance evaluation is also provided.

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IS Journal 2014 Journal Article

Self-Organizing Networks: From Bio-Inspired to Social-Driven

Dongliang Duan
Liuqing Yang
Yang Cao
Jiaolong Wei
Xiang Cheng

In today's complicated world of wireless networking, rapid changes and steep challenges could lead to too many users flocking to one or only a few networks, thereby leaving some potentially useful service providers out of the picture. To avoid such negative effects, it might be worthwhile to transition from the idea of bio-inspired to social-driven networking. Here, the authors outline a few rules to help with the transition.

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IS Journal 2014 Journal Article

Social Network Services for Rail Traffic Applications

Bo Ai
Xiang Cheng
Liuqing Yang
Zhang-Dui Zhong
Jian-Wen Ding
Hua Song

With the rapid development of high-speed railway, subway, city railway, and intercity rail, how to ensure the reliability, safety, and comfort of such rail traffic transportation systems becomes the focus. Meanwhile, the social network service (SNS) is advancing so fast all over the world. How to combine the characteristics of rail traffic transportation systems with that of the social networks, which enable them to play an important role in the rail traffic operations, will become one of the core problems in the future development of intelligent rail traffic transportation systems. Based on the business requirements, the reliable transmission of information, and the characteristics of the rail traffic transportation systems, this article proposes a new concept of "dedicated social network" whose information are exchanged within the internal intranet. A novel social network architecture for rail traffic transportation system is proposed, including the SNS Management Center, a dedicated social network for internal information exchanges among the drivers, the dispatchers, and the train control center, the SNS for the information exchanges among the train marshals, crews, and passengers. The proposed framework attempts to exploit social networks in an active manner in the field of rail traffic transportation systems to make the system safer, more secure, reliable, comfortable, and humane.

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