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Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r.t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio. Extensive experiments on real-world datasets show that our adaptive algorithms achieve larger influence spread than non-adaptive and other adaptive algorithms (up to a maximum of 116 percent). [ABSTRACT FROM AUTHOR]

This article studies the problem of RFID-based gesture recognition, which is practically important in various human-computer interaction scenarios, for example, smart homes, intelligent logistics, and smart cities. However, the existing solutions normally suffer from two major limitations: the model-driven methods are sensitive to specific environmental factors, and usually do not adapt well to a complex scenario that is full of multipath; the data-driven methods normally need the collection of massive RFID training data, and deploying the model in the remote cloud leads to long response delay. To overcome the above limitations, this article proposes a cross-domain augmentation-based AI learning (CAL) framework in the context of cloud-edge computing. In the CAL framework, we can simulate massive RFID phase profiles by converting the computer vision data that contains the gesture movement information, instead of costing lots of manpower to actually collect RFID training data. The simulated RFID phase profiles are used to train an AI model in the high-performance cloud. Note that since many sources of this kind of computer vision data are available online, we actually do not even need any manpower to collect training data. To achieve time-efficient recognition, knowledge distillation is applied to get a light and accurate model, which is deployed at the edge side. Thus, recognition response delay can be significantly reduced because the edge server where the AI model is actually deployed is much closer to users than the cloud server. We use commercial off-the-shelf RFID, Kinect, a high-performance server, and a laptop to implement the CAL framework. Extensive experiments are conducted to evaluate the performance of CAL. The results reveal that gesture recognition accuracy of CAL can reach nearly 90 percent without collection of any RFID training data. [ABSTRACT FROM AUTHOR]

Just like everything in nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could 'feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles. [ABSTRACT FROM AUTHOR]

Multicast tree is a key structure for data dissemination from one source to multiple receivers in wireless networks. Minimum length multica modeled as the Steiner tree problem, and is proven to be NP-hard. In this paper, we explore how to efficiently generate minimum length multi wireless sensor networks (WSNs), where only limited knowledge of network topology is available at each node. We design and analyze a simple algorithm, which we call toward source tree (TST), to build multicast trees in WSNs. We show three metrics of TST algorithm, i.e., running and energy efficiency. We prove that its running time is O(\sqrt n\log n) , the best among all existing solutions to our best knowledge. We prove that TST tree length is in the same order as Steiner tree, which give a theoretical upper bound and use simulations to show the ratio be only 1.114 when nodes are uniformly distributed. We evaluate energy efficiency in terms of message complexity and the number of forwarding prove that they are both order-optimal. We give an efficient way to construct multicast tree in support of transmission of voluminous data. [ABSTRACT FROM PUBLISHER]

This paper investigates random walk mobility models with location heterogeneity, where different locations may have different neighboring regions. We consider $n$ locations in a one-dimension network and investigate two cases, i.e., full-range locations where nodes situated have the capability to shuffle throughout the network and long-range locations where nodes are allowed to move to positions nearby within a certain range. In the former situation, with the exact expressions derived, we find location heterogeneity has a critical impact on the first hitting time of random walk, varying from \Theta (n)$ to \Theta \left(n^3\right)$ according to different extent of heterogeneity. The result covers, as two special cases, both the classic independent and identically distributed (i.i.d) mobility and traditional random walk when varying the number of full-range locations. In the latter one, our asymptotic results on both the first crossing time and cover time suggest that they are inversely proportional to the range of neighboring region r and \propto r^{-1} , respectively). Furthermore, with multiple concurrent random walks introduced, the first hitting time can be drastically decreased and the effect is strengthened if combined with location heterogeneity. In addition, our investigation into the stationary distribution of nodes indicates that the uniformity no longer holds due to different transition probabilities, as a result of location heterogeneity. We also conduct extensive simulation results to verify our observations and enhance the understanding on the impact of network parameters. Based on the insights obtained, we move forward to investigate the impact of location heterogeneity in two-dimension networks. [ABSTRACT FROM PUBLISHER]

This paper characterizes an evolving network with growing number of nodes, which are associated with each other through social relations but employ transmission via wireless communications. The network exhibits social relations between nodes by attaching a new arriving node to a sample node already existing as well as some nodes the sample directly links to. We study capacity in this evolving network in terms of both geographic distribution of nodes and traffic patterns. Our results show that in heterogeneous geographic distribution where nodes having strong social relations tend to locate more closer, the corresponding capacity is significantly impacted by both social relations and network evolution. In particular, with appropriate control on the initial number of nodes as well as links between them, it is even possible to achieve almost constant per-node capacity except for a poly-logarithmic factor. For homogeneous geographic topology where nodes' positions exhibit uniform distribution, social relations among nodes is useless for improving capacity, which is mainly affected by network evolution and therefore degrades sharply with time. To our best knowledge, this is the first work studying capacity from the perspective of social evolving network. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of multicast scaling performance in multichannel multiradio (MC-MR) networks. Under the assumption of both limited bandwidth and node tunability, a total fixed bandwidth (W) is equally split into (c) channels with (0

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability. [ABSTRACT FROM AUTHOR]

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of 'X-ray' scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted 'idea tree' that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development. [ABSTRACT FROM AUTHOR]

With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases. [ABSTRACT FROM AUTHOR]

The rapid growth of online social networks (OSNs) has ultimately facilitated information spreading and changed the economics of mobile networks. It is important to understand how to spread information as widely as possible. In this paper, we aim to seek powerful information initial spreaders with an efficient manner. We use the mean-field theory to characterize the process of information spreading based on the Susceptible Infected (SI) model and validate that the prevalence of information depends on the network density. Inspired by this result, we seek the initial spreaders from closely integrated groups of nodes, i.e., dense groups (DGs). In OSNs, DGs distribute dispersedly over the network, so our approach can be fulfilled in a distributed way by seeking the spreaders in each DG. We first design a DG Generating Algorithm to detect DGs, where nodes within the DG have more internal connections than external ones. Second, based on the detected DGs, we design a criterion to seek powerful initial spreaders from each DG. We conduct experiments as well as statistical analysis on real OSNs. The results show that our approach provides a satisfactory performance as well as computational efficiency. [ABSTRACT FROM AUTHOR]

Recently, security issues in dynamic spectrum access (DSA) received more and more attention. One type of threat, which we call the greedy resource occupancy, has long been ignored so far. Specifically, a secondary user (SU) may greedily occupy spectrum for a long time, which makes other SUs suffer additional waiting time to access the spectrum and leads to congestion or breakdown. In this paper, we investigated the DSA networks with greedy users. The primary user's privilege of accessing the networks at any time is also taken into account. Accordingly, the process of DSA is treated as a privilege-existing multidimensional queueing process. First, we propose an analysis framework for the queueing system and exploit it to investigate impacts of greedy SUs (GSUs) on system performance, i.e., the queue size and waiting time in queue. Second, we propose a wavelet based method to detect the greedy resource occupancy threat quickly. Since a sudden change in average occupancy time of the GSU will produce dramatic performance degradation, the GSU prefers to increase his occupancy time in a gradual manner to avoid the easy detection. Third, to protect honest SUs from being affected by GSUs, a protection strategy is put forward, and the system performance with the strategy has also been analyzed by the queueing analysis. Last, we conduct numerical analyses to demonstrate explicit impacts of GSUs on the system performance as well as efficacy of the protection strategy. We also carry out simulations on the detecting approach, and the results verify effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method. [ABSTRACT FROM PUBLISHER]

This paper investigates throughput and delay based on a traffic pattern, called convergecast, where each of the n nodes in the network acts as a destination with k randomly chosen sources corresponding to it. Adopting Multiple-Input-Multiple-Output (MIMO) technology, we devise two many-to-one cooperative schemes under convergecast for both static and mobile ad hoc networks (MANETs), respectively. We call them Convergimo Schemes. In static networks, our Convergimo scheme highly utilizes hierarchical cooperation MIMO transmission. This feature overcomes the bottleneck which hinders convergecast traffic from yielding ideal performance in traditional ad hoc network, by turning the originally interfering signals into interference-resistant ones. It helps to achieve an aggregate throughput up to \Omega (n^1-\epsilon ) for any \epsilon >0. In the mobile ad hoc case, our Convergimo scheme characterizes on joint transmission from multiple nodes to multiple receivers. With optimal network division where the number of nodes per cell is constantly bounded, the achievable per-node throughput can reach \Theta (1) with the corresponding delay reduced to \Theta (k). The gain comes from the strong and intelligent cooperation between nodes in our scheme, along with the maximum number of concurrent active cells and the shortest waiting time before transmission for each node within a cell. This increases the chances for each destination to receive the data it needs with minimum overhead on extra transmission. Moreover, our converge-based analysis well unifies and generalizes previous work since the results derived from convergecast in our schemes can also cover other traffic patterns. Last but not the least, our schemes are of interest not only from a theoretical perspective but also provide useful theoretical guidelines to future design of MIMO schemes in wireless networks. [ABSTRACT FROM AUTHOR]

Neural networks have achieved great success in many tasks, including data classification and pattern recognition. However, how neural networks work and what representations they learn are still not fully understood. For any data sample fed into a neural network, we wondered how its corresponding vectors expanded by activated neurons change throughout the layers and why the final output vector could be classified or clustered. To formally answer these questions, we define the data sample outputs of each layer as activation vectors and the space expanded by them as the activation space. Then, we investigate the geometric structure of the high-dimensional activation spaces of neural networks by studying the geometric characters of the massive activation vectors through approximated convex hulls. We find that the different layers of neural networks have different roles, where the former and latter layers can disperse and gather data points, respectively. Moreover, we also propose a novel classification method based on the geometric structures of activation spaces, called nearest convex hull (NCH) classification, for the activation vectors in each layer of a neural network. The empirical results show that the geometric structure can indeed be utilized for classification and often outperforms original neural networks. Finally, we demonstrate that the relationship among the convex hulls of different classes could be a good metric to help us optimize neural networks in terms of over-fitting detection and network structure simplification. [ABSTRACT FROM AUTHOR]

Pathogens integrate multiple environmental signals to navigate the host and control the expression of virulence genes. In this process, small regulatory noncoding RNAs (sRNAs) may function in gene expression as post-transcriptional regulators. In this study, the sRNA Xonc3711 functioned in the response of the rice pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), to oxidative stress. Xonc3711 repressed production of the DNA-binding protein Xoc_3982 by binding to the xoc_3982 mRNA within the coding region. Mutational analysis showed that regulation required an antisense interaction between Xonc3711 and xoc_3982 mRNA, and RNase E was needed for degradation of the xoc_3982 transcript. Deletion of Xonc3711 resulted in a lower tolerance to oxidative stress due to the repression of flagella-associated genes and reduced biofilm formation. Furthermore, ChIP-seq and electrophoretic mobility shift assays showed that Xoc_3982 repressed the transcription of effector xopC2, which contributes to virulence in Xoc BLS256. This study describes how sRNA Xonc3711 modulates multiple traits in Xoc via signals perceived from the external environment. Author summary: Small, stable RNA species perform diverse functions in both prokaryotes and eukaryotes. In this study, the sRNA Xonc3711 decreased the production of DNA-binding protein Xoc_3982 in the bacterium Xanthomonas oryzae pv. oryzicola (Xoc) by base pairing with the xoc_3982 transcript. When Xonc3711 was mutated, Xoc was impaired in its ability to form flagella and produce biofilms, which reduced Xoc tolerance to oxidative stress. We also discovered that the DNA-binding protein Xoc_3982 represses the expression of xopC2, which encodes an effector protein, and reduces its expression. Our results show that Xonc3711 modulates and integrates multiple systems in Xoc to protect cells from oxidative damage. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSNs) have become an efficient media to disseminate content like news and advertisements. In this paper, we are interested in investigating the open problem of how does the content popularity influence the information coverage exactly. Specifically, we assume that each user has $q$ friends and forwards the received content to each of his friends with the forwarding probability $p$ , where $p$ is only determined by the content popularity. Based on a geography-aware social model leveraging the forwarding behavior of OSNs users, we explore the exact relationship between the content popularity and the information coverage by the method of network partitions. Then, we mainly have the following observations: 1) The information coverage is only related to $p$ and $q$ ; 2) In order to disseminate the content to a constant portion of all users within the network, the forwarding probability $p$ should be no less than $\Theta \left(\frac{1}{q}\right)$. Extensive evaluations on both the generated data and the real OSN topology validate our observations. [ABSTRACT FROM AUTHOR]

The widespread of smart phones has brought new social-aware features to wireless networks. Users prefer to forward traffic to their social contacts in social-aware networks, which is highly different from the traditional uniform traffic pattern. To this end, we analyze a social-aware wireless network, which is modeled by the social contacts between nodes, and explore its impact on network throughput capacity. We present that it can refine social contacts, thus improving the network capacity. Moreover, directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. We derive the throughput capacity for social-aware networks with single-beam and multi-beam directional antenna, respectively. For single-beam directional antenna, we prove that when wireless networks are dominated by traffics of short-distance social contact, the throughput capacity can be promoted from order of (\Theta (1/\theta ^2n\,\text log\,n)^1/2) to \Theta (1/\theta ^2 \text log\,n) . In addition, when the beamwidth \theta is at order of \Omega ({1}/{({\log {n}})^{1/2}}) , the throughput capacity is a constant. Thus, the wireless network is scalable. For multi-beam antennas, we prove that when sidelobe gain G_{s} is at order of o(Gm) , where Gm is main lobe gain, compared with the single-beam case, the network capacity can achieve the order of \Theta (1/\theta ^2\text log\,n) . [ABSTRACT FROM AUTHOR]

A core mission of university education is to train students' problem solving abilities, or in some cases, students' research skills. To achieve this, educators assign students certain experiment courses, where students complete a task following preset procedures. However, such courses often employ outdated teaching materials that are far from cutting-edge problems. Meanwhile, a preset experiment procedure cannot evoke students' innovations. In this article we introduce an experimental course we opened at Shanghai Jiao Tong University for junior-year students -- the Research-Oriented Course (ROC). ROC is based on a cutting-edge research project and includes an Introduction -- Demonstration -- Research -- Evaluation four-phase course as well as the T -- W -- R three-dimensional evaluation criteria. Evaluations showed that students finished their research tasks satisfactorily and they improved their research skills through this process. [ABSTRACT FROM PUBLISHER]

Due to the nature of a wireless medium, a wireless channel is susceptible to malicious nodes, which encourages the consideration of secrecy in many applications such as satellite networks, social networking services, etc. Motivated by the social characteristic that makes nodes more likely to communicate with nearby neighbors, we study the secrecy capacity of large-scale wireless networks with social relationships. We consider two distributions of legitimate nodes: They are homogeneously placed based on a Poisson point process (PPP), or they are inhomogeneously distributed as multiclustering topology. In particular, we consider two scenarios: 1) the noncolluding case, where each eavesdropper decodes its receiving message independently; and 2) the colluding case, where eavesdroppers can cooperate to decode the message. We adopt a rank-based model to describe the social characteristic and investigate its impact on the secrecy capacity using the technique of self-interference cancelation. We employ a passive attack model, where eavesdroppers can only listen to transmissions among legitimate nodes. Our important findings include the following: 1) In the noncolluding case, secrecy capacity is not influenced by eavesdroppers in order sense, but it varies with the density of eavesdroppers and social impacts in the colluding case. 2) The proposed scheme can achieve optimal secrecy throughput neglecting polylogarithmic factor in homogeneous wireless networks. [ABSTRACT FROM PUBLISHER]

In this paper, we study the throughput and delay in wireless cognitive social networks. Specifically, we consider a common scenario for cognitive radio networks (CRNs) where the primary and secondary networks operate at the same time and space and share the spectrum. On this basis, we integrate a social relationship into the CRN where each source node selects its destination upon a rank-based model, which captures the social characteristic well. By applying a cellular time-division multiple-access scheduling scheme, we first characterize the distinct traffic pattern caused by the social relationships between nodes. Then, we derive the achievable throughput and delay for both primary and secondary networks under the new network setting. In addition, we also study the cognitive social networks with infrastructure where $l=o$ $(n)$ base stations are regularly deployed within the primary network. Given a probabilistic routing strategy, throughput of the proposed network is recalculated. Particularly, due to the social relationships between nodes, we reveal that a larger $l$ is required if we expect a significant capacity gain within the primary network compared with previous works. [ABSTRACT FROM PUBLISHER]

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis. [ABSTRACT FROM AUTHOR]

A correction to the article "Asymptotic Analysis on Throughput and Delay in Cognitive Social Networks" that was published in the August 2014 issue is presented.

In this paper, we study a unified mobility model for mobile multicast (MotionCast) with n nodes, and k destinations for each multicast session. This model considers nodes which can either serve in a local region or move around globally, with a restricted speed R. In other words, there are two particular forms: Local-based Speed-Restricted Model (LSRM) and Global-based Speed-Restricted Model (GSRM). We find that there is a special turning point when mobility speed varies from zero to the scale of network. For LSRM, as R increases, the delay-capacity trade-off ratio decreases iff R is greater than the turning point \Theta (\sqrt1\over k ); For GSRM, as R increases, the trade-off ratio decreases iff R is smaller than the turning point, where the turning point is located at \Theta (k^0.25\over \sqrtn ) when k=o(n^2\over 3 ), and at \Theta (k\over n ) when k=\omega (n^2\over 3 ). As k increases from 1 to n-1, the region that mobility can improve delay-capacity trade-off is enlarged. When R=\Theta (1), the optimal delay-capacity trade-off ratio is achieved. This paper presents a general approach to study the performance of wireless networks under more flexible mobility models. [ABSTRACT FROM AUTHOR]