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Efficient frequent pattern mining from big data and its applicationsJiang, Fan January 2016 (has links)
Frequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide variety of valuable data of different veracities can be easily collected or generated at high velocity in various real-life applications. Among these 5V's of big data, I focus on handling high volumes of big data in my Ph.D. thesis. Specifically, I design and implement a new efficient frequent pattern mining algorithmic technique called B-mine, which overcomes some of the aforementioned drawbacks and achieves better performance when compared with existing algorithms. I also extend my B-mine algorithm into a family of algorithms that can perform big data mining efficiently. Moreover, I design four different frameworks that apply this family of algorithms to the real-life application of social network mining. Evaluation results show the efficiency and practicality of all these algorithms. / February 2017
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Parallel Mining and Analysis of Triangles and Communities in Big NetworksArifuzzaman, S M. 19 August 2016 (has links)
A network (graph) is a powerful abstraction for interactions among entities in a system. Examples include various social, biological, collaboration, citation, and co-purchase networks. Real-world networks are often characterized by an abundance of triangles and the existence of well-structured communities. Thus, counting triangles and detecting communities in networks have become important algorithmic problems in network mining and analysis. In the era of big data, the network data emerged from numerous scientific disciplines are very large. Online social networks such as Twitter and Facebook have millions to billions of users. Such massive networks often do not fit in the main memory of a single machine, and the existing sequential methods might take a prohibitively large runtime. This motivates the need for scalable parallel algorithms for mining and analysis.
We design MPI-based distributed-memory parallel algorithms for counting triangles and detecting communities in big networks and present related analysis. The dissertation consists of four parts. In Part I, we devise parallel algorithms for counting and enumerating triangles. The first algorithm employs an overlapping partitioning scheme and novel load-balancing schemes leading to a fast algorithm. We also design a space-efficient algorithm using non-overlapping partitioning and an efficient communication scheme. This space efficiency allows the algorithm to work on even larger networks. We then present our third parallel algorithm based on dynamic load balancing. All these algorithms work on big networks, scale to a large number of processors, and demonstrate very good speedups. An important property, very related to triangles, of many real-world networks is high transitivity, which states that two nodes having common neighbors tend to become neighbors themselves. In Part II, we characterize networks by quantifying the number of common neighbors and demonstrate its relationship to community structure of networks. In Part III, we design parallel algorithms for detecting communities in big networks. We propose efficient load balancing and communication approaches, which lead to fast and scalable algorithms. Finally, in Part IV, we present scalable parallel algorithms for a useful graph preprocessing problem-- converting edge list to adjacency list. We present non-trivial parallelization with efficient HPC-based techniques leading to fast and space-efficient algorithms. / Ph. D.
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由史料中探勘社會網絡:以乾隆時期為例 / Social Network Mining from Historical Documents- by Example during Qianlong's Reign朱政吉, Chu, Cheng Ji Unknown Date (has links)
古今中外歷史中,在政治權力的結構裡,除了在最上位的領袖外,其下的文武百官,往往根據其職份或私交等情況,自成人際關係網絡。然而,依照每個人在網絡中位置的不同,重要程度也有所不同。在網絡中扮演重要角色者,除了代表其人際關係愈複雜外,同時也暗示其政治影響力愈大。這些人物往往也就是足以影響當時政治的「權臣」。然而在歷史上,有些皇帝的在位時間較長、統治時間較久。在其統治期間,可能因為皇帝本身,或政治環境遞嬗等因素,使得不同的時期有不同「權臣」,或是其晚年才出現明顯的「權臣」。本論文便是基於這樣的歷史現象,研究由史料中探勘當時的人脈網絡。我們先從文本中,自動擷取出人名。然後,藉由人物在文本中與其他人物的共現場合,建立歷史人物的人脈網絡,接著利用社會網絡分析的理論基礎,分析這些網絡,進而在網絡中找出權臣,以及偵測政治權力結構的變化,為時代作出分期。本研究選用的文本為《清實錄》中的《高宗純皇帝實錄》,意欲以清高宗 (乾隆)時期為例,探勘該朝的人脈網絡,完成上述之工作。希望這樣的研究,可以在中國政治制度史等研究上,協助史學研究者。 / In power structure from ancient times to the present, officials who under the leader usually take part in the social network according to their positions or friendship. However, the importance of each person is different by their locations in network. The people who play important roles in network have complex interpersonal relationship as well as high influence in political situation. We call them "chief counselors." But in the history, some emperors reign for extremely many years. Due to some causes, such like emperor himself or changing of political circumstances, there could be several different chief counselors during their reign. This thesis focuses on social network mining from historical documents in view of above-mentioned historical phenomenon. After extracting person names from the corpus, we can construct social network by co-occurrence of people, then to find chief counselors and detect transition of power structure by Social Network Analysis. The "Veritable Records of Gaozong" is taken as the example for experiments and the result of effectiveness analysis demonstrates that the proposed methods are helpful to assist historian for historical research.
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Network Mining Approach to Cancer Biomarker DiscoveryUppalapati, Praneeth 03 September 2010 (has links)
No description available.
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Contributions à l'étude des réseaux sociaux : propagation, fouille, collecte de données / Contributions to the study of social networks : propagation,mining,data collectionStattner, Erick 10 December 2012 (has links)
Le concept de réseau offre un modèle de représentation pour une grande variété d'objets et de systèmes, aussi bien naturels que sociaux, dans lesquels un ensemble d'entités homogènes ou hétérogènes interagissent entre elles. Il est aujourd'hui employé couramment pour désigner divers types de structures relationnelles. Pourtant, si chacun a une idée plus ou moins précise de ce qu'est un réseau, nous ignorons encore souvent les implications qu'ont ces structures dans de nombreux phénomènes du monde qui nous entoure. C'est par exemple le cas de processus tels que la diffusion d'une rumeur, la transmission d'une maladie, ou même l'émergence de sujets d'intérêt commun à un groupe d'individus, dans lesquels les relations que maintiennent les individus entre eux et leur nature s'avèrent souvent être les principaux facteurs déterminants l'évolution du phénomène. C'est ainsi que l'étude des réseaux est devenue l'un des domaines émergents du 21e siècle appelé la "Science des réseaux". Dans ce mémoire, nous abordons trois problèmes de la science des réseaux: le problème de la diffusion dans les réseaux sociaux, où nous nous sommes intéressés plus particulièrement à l'impact de la dynamique du réseau sur le processus de diffusion, le problème de l'analyse des réseaux sociaux, dans lequel nous avons proposé une solution pour tirer parti de l'ensemble des informations disponibles en combinant les informations sur la structure du réseau et les attributs des noeuds et le problème central de la collecte de données sociales, où nous nous sommes intéressés au cas particulier de la collecte de données en milieux sauvages / The concept of network provides a model for representing a wide variety of objects and systems, both natural and social, in which a set of homogeneous or heterogeneous entities interact. It is now widely used to describe various kinds of relational structures. However, if everyone has an idea of the concept of network, we often ignore the implications that these structures have in real world phenomena. This is for example the case of processes such as the spread of a rumor, the disease transmission, or even the emergence of subjects of common interest for a group of individuals, in which the relations maintained between individuals, and their nature, often prove to be the main factors determining the evolution of the phenomenon. This is the reason why the study of networks has become one of the emerging areas in the 21st century called the "Science of networks." ln this thesis, we address three issues of the domain of the science of networks: the problem of diffusion in social networks, where we have addressed more particularly the impact of the network dynamics on the diffusion process, the problem of the analysis of social networks, in which we have proposed a solution to take full advantage of all information available on the network by combining information on both structure and node attributes and the central problem of the social data collection, for which we have focused on the particular case of the data collection in a wild environment.
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Computational Network Mining in High-Risk Patients with Multiple MyelomaYu, Christina Y. January 2020 (has links)
No description available.
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Relational Representation Learning Incorporating Textual Communication for Social NetworksYi-Yu Lai (10157291) 01 March 2021 (has links)
<div>Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Many methods have been proposed in this area to learn continuous low-dimensional embedding of nodes, edges or relations in social and information networks. However, most previous network RL methods neglect social signals, such as textual communication between users (nodes). Unlike more typical binary features on edges, such as post likes and retweet actions, social signals are more varied and contain ambiguous information. This makes it more challenging to incorporate them into RL methods, but the ability to quantify social signals should allow RL methods to better capture the implicit relationships among real people in social networks. Second, most previous work in network RL has focused on learning from homogeneous networks (i.e., single type of node, edge, role, and direction) and thus, most existing RL methods cannot capture the heterogeneous nature of relationships in social networks. Based on these identified gaps, this thesis aims to study the feasibility of incorporating heterogeneous information, e.g., texts, attributes, multiple relations and edge types (directions), to learn more accurate, fine-grained network representations. </div><div> </div><div>In this dissertation, we discuss a preliminary study and outline three major works that aim to incorporate textual interactions to improve relational representation learning. The preliminary study learns a joint representation that captures the textual similarity in content between interacting nodes. The promising results motivate us to pursue broader research on using social signals for representation learning. The first major component aims to learn explicit node and relation embeddings in social networks. Traditional knowledge graph (KG) completion models learn latent representations of entities and relations by interpreting them as translations operating on the embedding of the entities. However, existing approaches do not consider textual communications between users, which contain valuable information to provide meaning and context for social relationships. We propose a novel approach that incorporates textual interactions between each pair of users to improve representation learning of both users and relationships. The second major component focuses on analyzing how users interact with each other via natural language content. Although the data is interconnected and dependent, previous research has primarily focused on modeling the social network behavior separately from the textual content. In this work, we model the data in a holistic way, taking into account the connections between the social behavior of users and the content generated when they interact, by learning a joint embedding over user characteristics and user language. In the third major component, we consider the task of learning edge representations in social networks. Edge representations are especially beneficial as we need to describe or explain the relationships, activities, and interactions among users. However, previous work in this area lack well-defined edge representations and ignore the relational signals over multiple views of social networks, which typically contain multi-view contexts (due to multiple edge types) that need to be considered when learning the representation. We propose a new methodology that captures asymmetry in multiple views by learning well-defined edge representations and incorporates textual communications to identify multiple sources of social signals that moderate the impact of different views between users.</div>
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由職官年表中利用循序共現樣式探勘人脈網絡 / Social network analysis from official chronology using sequential co-occurrence pattern mining宋邡熏, Song, Fang Shiun Unknown Date (has links)
在政治權力結構中,權臣與派系在其政治人物的社會網絡中扮演重要的角色。本論文研究由職官年表中探勘權臣與派系。我們提出資料探勘演算法由職官年表中探勘循序共現樣式,以探勘出政府官員官職陞貶的共現關係。接著根據所探勘出的循序共現樣式,建立官員之間的社會網絡。透過社會網絡分析中的網絡中心性與社群偵測分別探勘出權臣與派系。本論文以清康熙時期的職官年表實驗驗證。透過視覺化分析顯示本論文所提出的方法有助於歷史學者的研究。 / In a power structure, chief officials and cliques play important roles in the social network and have high influence on politics. This thesis proposes an approach of social network mining from official chronologies to discover the chief officials and the cliques. We propose and develop the algorithm to discover the sequential co-occurrence patterns from official chronologies. Then the social network is constructed based on the discovered sequential co-occurrence patterns. Chief officials are discovered by network centrality analysis while cliques are discovered by community analysis of the constructed social network. The official chronology of Kangxi Emperor is taken as an example for experiments and the visualization analysis demonstrates that the proposed methods are helpful to assist historian for historical research.
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