基于边图的线性流重叠社区发现算法

重叠网络的社区发现是复杂网络研究中的重要问题。为了提高网络中重叠社区发现的时间效率，提出一种基于边图的线性流重叠社区发现算法LBSA。算法首先对于边图网络中的边进行随机的依次处理，完成节点的初步社区划分，再将其中重叠小社区合并到相似度最大的其他大社区中得到最终的社区。通过以上步骤，算法能够以接近线性的时间复杂度得到网络的重叠结构。从最终的实验结果来看，与其他算法相比，该算法能够在更短的时间有质量地发现网络中的重叠社区。     

松辽盆地伏龙泉断陷边界断层构造反转率

盆地边界断层通常为同沉积断层，同沉积断层活动时间相对短且具有间歇性，地层沉积时间相对较长，这决定了断陷盆地的地层分布格局。通过分析同沉积断层理论模式，认为断陷缓坡带地层厚度与边界断层下盘厚度相当，边界断层下盘通常缺失与上盘对应的地层，可选择断陷盆地缓坡带地层厚度代替边界断层下盘厚度，建立虚拟下盘厚度格架，然后运用位移-距离曲线等方法对边界断层开展定量研究。伏龙泉断陷位于松辽盆地东南部，为东断西超的半地堑盆地，发育4条边界断层，依次控制了4个沉积次洼，4条边界断层的活动强度和反转程度存在差异。伏龙泉断陷经历了断陷期、拗陷期、反转期三个主要的构造期次，分别对应于边界断层强烈活动、停止活动、反转活动3个演化阶段，发生于白垩纪末的反转活动使得边界断层再次活化，形成油气运移通道，深层油气向上运移到浅层圈闭中，形成次生油气藏。通过定量计算伏龙泉断陷各边界断层的反转率，结合油气富集规律认识，认为反转率适中的区域最有利于次生油气藏的形成和保存。     

A distributed command governor based on graph colorability theory

In this paper, a distributed command governor (CG) strategy is introduced that, by the use of graph colorability theory, improves the scalability property and the performance of recently introduced distributed noncooperative sequential CG strategies. The latter are characterized by the fact that only 1 agent at a decision time is allowed to update its command, whereas all the others keep applying their previously computed commands. The scalability of these early CG distributed schemes and their performance are limited because the structure of the constraints is not taken into account in their implementation. Here, by exploiting the idea that agents that are not directly coupled by the constraints can simultaneously update their control actions, the agents in the network are grouped into particular subsets (turns). At each time instant, on the basis of a round‐robin policy, all agents belonging to a turn are allowed to update simultaneously their commands, whereas agents in other turns keep applying their previous commands. Then, a turn‐based distributed CG strategy is proposed and its main properties are analyzed. Graph colorability theory is used to determine the minimal number of turns and to distribute each agent in at least a turn. A novel graph colorability problem that allows one to maximize the frequency at which agents can update their commands is proposed and discussed. A final example is presented to illustrate the effectiveness of the proposed strategy.     

Supporting ontology adaptation and versioning based on a graph of relevance

Ontologies recently have become a topic of interest in computer science since they are seen as a semantic support to explicit and enrich data-models as well as to ensure interoperability of data. Moreover, supporting ontology adaptation becomes essential and extremely important, mainly when using ontologies in changing environments. An important issue when dealing with ontology adaptation is the management of several versions. Ontology versioning is a complex and multifaceted problem as it should take into account change management, versions storage and access, consistency issues, etc. The purpose of this paper is to propose an approach and tool for ontology adaptation and versioning. A series of techniques are proposed to ‘safely’ evolve a given ontology and produce a new consistent version. The ontology versions are ordered in a graph according to their relevance. The relevance is computed based on four criteria: conceptualisation, usage frequency, abstraction and completeness. The techniques to carry out the versioning process are implemented in the Consistology tool, which has been developed to assist users in expressing adaptation requirements and managing ontology versions.     

Optimizing the parallel adaptive indexing algorithm on multi-core CPUs

An improved parallel adaptive indexing algorithm on multi-core CPUs is proposed to solve the problems that the parallel adaptive indexing algorithms cannot take full advantage of the CMP's parallel execution resource, and properly process the sequential query pattern. Based on the optimization of the Refined Partition Merge algorithm, our improved parallel adaptive indexing algorithm combines the Parallel Database Cracking method with the Refined Partition Merge algorithm. In our algorithm, when fewer data chunks are in the index, we use the optimized Refined Partition Merge algorithm so as to reduce the probability of conflict between threads, decrease the waiting time, and increase the utilization of the threads, and when more data chunks are in the index, we use the Parallel Database Cracking method so as to take full advantage of the CMP's parallel execution resources. Besides, we propose an optimization for the robustness, which makes our algorithm suitable for two common query patterns. Experiments show that our method can reduce the query time by 25.7％～33.2％, and suit with common query patterns.     

Length two path-centred surface areas for the arrangement graph

We suggest the notion of H-centred surface area of a graph G, where H is a subgraph of G, i.e. the number of vertices in G at a certain distance from H, and focus on the special case when H is a length two path to derive an explicit formula for the length two path-centred surface areas of the general and scalable arrangement graph.     

Experiments on drawing 2-level hierarchical graphs

This paper studies different heuristics for drawing 2-level hierarchical graphs. Especially, we compare the barycenter and the median heuristics. We show that the barycenter heuristic clearly outperforms the median heuristic, although only the latter has a proved bound for the maximum error done when two vertices are ordered. Moreover, we improve a known heuristic, called the greedy switching, by introducing the barycenter heuristic as a preprocessing phase for it.     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

Session-based recommendation (SBR) and multi-behavior recommendation (MBR) are both important problems and have attracted the attention of many researchers and practitioners. Different from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics, heterogeneous SBR (HSBR) that exploits different types of behavioral information (e.g., examinations like clicks or browses, purchases, adds-to-carts and adds-to-favorites) in sequences is more consistent with real-world recommendation scenarios, but it is rarely studied. Early efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of behaviors. However, all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of behaviors. However, all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of behaviors. The limitation hinders the development of HSBR and results in unsatisfactory performance. As a response, we propose a novel behavior-aware graph neural network (BGNN) for HSBR. Our BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a session. Moreover, our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified way. We then conduct extensive empirical studies on three real-world datasets, and find that our BGNN outperforms the best baseline by 21.87%, 18.49%, and 37.16% on average correspondingly. A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our BGNN. An exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can easily and effectively be extended to multi-behavior scenarios.     

Partitioning road networks using density peak graphs: Efficiency vs. accuracy

Road traffic networks are rapidly growing in size with increasing complexities. To simplify their analysis in order to maintain smooth traffic, a large urban road network can be considered as a set of small sub-networks, which exhibit distinctive traffic flow patterns. In this paper, we propose a robust framework for spatial partitioning of large urban road networks based on traffic measures. For a given urban road network, we aim to identify the different sub-networks or partitions that exhibit homogeneous traffic patterns internally, but heterogeneous patterns to others externally. To this end, we develop a two-stage algorithm (referred as FaDSPa) within our framework. It first transforms the large road graph into a well-structured and condensed density peak graph (DPG) via density based clustering and link aggregation using traffic density and adjacency connectivity, respectively. Thereafter we apply our spectral theory based graph cut (referred as α-Cut) to partition the DPG and obtain the different sub-networks. Thus the framework applies the locally distributed computations of density based clustering to improve efficiency and the centralized global computations of spectral clustering to improve accuracy. We perform extensive experiments on real as well as synthetic datasets, and compare its performance with that of an existing road network partitioning method. Our results show that the proposed method outperforms the existing normalized cut based method for small road networks and provides impressive results for much larger networks, where other methods may face serious problems of time and space complexities.