Dynamics of Real-world Networks

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● Dynamics of Real-world Networks
● Committee members
● Network dynamics
● Large real world networks
● Questions we ask
● Our work: Network dynamics
● Our work: Goals
● Our work: Overview
● Our work: Impact and applications
● Outline
● Completed work: Overview
● G1 – Patterns: Densification
● G1 – Patterns: Shrinking diameters
● G2 – Models: Kronecker graphs
● Idea: Recursive graph generation
● Kronecker product: Graph
● Properties of Kronecker graphs
● G3 – Predictions: The problem
● Model estimation: approach
● Model estimation: solution
● Model estimation: experiments
● Completed work: Overview
● Information cascades
● Cascades: Questions
● Cascades in viral marketing
● Product recommendation network
● G1- Viral cascade shapes
● G1- Viral cascade sizes
● Does receiving more recommendations
increase the likelihood of buying?
● Cascades in the blogosphere
● G1- Blog cascade shapes
● G1- Blog cascade size
● G2- Blog cascades: model
● G3- Node selection for cascade detection
● Node selection: algorithm
● Outline
● Proposed work: Overview
● Proposed work: Communication networks
● Proposed work: Links & cascades
● Proposed work: Kronecker graphs
● Timeline
● References

نوع زبان: انگلیسی حجم: 3.91 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 47 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/06/15 11:30:13

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

network, cascade, graph, model, kronecker, node, dynamics, e, log, g۱, n, property,

توجه: این مطلب در تاریخ 2019/06/15 11:30:13 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

http://www.cs.cmu.edu/~jure/thesis/jure-proposal.ppt

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: network, cascade, graph, model, kronecker, node, dynamics, e, log, g۱, n, property,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

dynamics of real world networks jure leskovec machine learning department carnegie mellon university jure@cs.cmu.edu http www.cs.cmu.edu ~jure committee members christos faloutsos avrim blum jon kleinberg john lafferty network dynamics internet yeast protein interactions web citations sexual network friendship network food web who eats whom large real world networks instant messenger network n ۱۸ million nodes e ۱.۳ billion edges blog network n ۲.۵ million nodes e ۵ million edges autonomous systems n ۶ ۵ nodes e ۲۶ ۵ edges citation network of physics papers n ۳۱ nodes e ۳۵ edges recommendation network n ۳ million nodes e ۱۶ million edges questions we ask do networks follow patterns as they grow how to generate realistic graphs how does influence spread over the network chains stars how to find select nodes to detect cascades our work network dynamics our research focuses on analyzing and modeling the structure evolution and dynamics of large real world networks evolution growth and evolution of networks cascades processes taking place on networks observe static and temporal properties design network generative models information propagation and cascades find common sub network properties our work goals ۳ parts goals g۱ what are interesting statistical properties of network structure e.g. ۶ degrees g۲ what is a good tractable model e.g. preferential attachment g۳ use models and findings to predict future behavior e.g. node immunization our work overview s۱ dynamics of network evolution s۲ dynamics of processes on networks g۱ patterns g۲ models g۳ predictions our work overview s۱ dynamics of network evolution s۲ dynamics of processes on networks g۱ patterns kdd ‘ ۵ tkdd ’ ۷ pkdd ‘ ۶ acm ec ’ ۶ g۲ models kdd ‘ ۵ pakdd ’ ۵ sdm ‘ ۷ tweb ’ ۷ g۳ predictions kdd ‘ ۶ icml ’ ۷ www ‘ ۷ submission to kdd our work impact and applications structural properties abnormality detection graph models graph generation graph sampling and extrapolations anonymization cascades node selection and targeting outbreak detection outline introduction completed work s۱ network structure and evolution s۲ network cascades proposed work kronecker time evolving graphs large online communication networks links and information cascades conclusion completed work overview s۱ dynamics of network evolution s۲ dynamics of processes on networks g۱ patterns densification shrinking diameters cascade shape and size g۲ models forest fire kronecker graphs cascade generation model g۳ predictions estimating kronecker parameters selecting nodes for detecting cascades completed work overview s۱ dynamics of network evolution s۲ dynamics of processes on networks g۱ patterns densification shrinking diameters cascade shape and size g۲ models forest fire kronecker graphs cascade generation model g۳ predictions estimating kronecker parameters selecting nodes for detecting cascades g۱ patterns densification what is the relation between the number of nodes and the edges over time networks are denser over time densification power law a … densification exponent ۱ ≤ a ≤ ۲ a ۱ linear growth – constant degree a ۲ quadratic growth – clique internet citations log n t log e t a ۱.۲ a ۱.۷ log n t log e t g۱ patterns shrinking diameters intuition and prior work say that distances between the nodes slowly grow as the network grows like log n diameter shrinks or stabilizes over time as the network grows the distances between nodes slowly decrease internet citations time diameter diameter size of the graph g۲ models kronecker graphs want to have a model that can generate a realistic graph with realistic growth patterns for static networks patterns for evolving networks the model should be analytically tractable we can prove properties of graphs the model generates computationally tractable we can estimate parameters idea recursive graph generation try to mimic recursive graph community growth because self similarity leads to power laws there are many obvious but wrong ways does not densify has increasing diameter kronecker product is a way of generating self similar matrices initial graph recursive expansion kronecker product graph adjacency matrix intermediate stage adjacency matrix ۹x۹ ۳x۳ kronecker product graph continuing multiplying with g۱ we obtain g۴ and so on … g۴ adjacency matrix properties of kronecker graphs we show that kronecker multiplication generates graphs that have properties of static networks power law degree distribution power law eigenvalue and eigenvector distribution small diameter properties of dynamic networks densification power law shrinking stabilizing diameter this means shapes of the distributions match but the properties are not independent how do we set the initiator to match the real graph      g۳ predictions the problem we want to generate realistic networks g۱ what are the relevant properties g۲ what is a good tractable model g۳ how can we fit the model find parameters compare some property e.g. degree distribution given a real network generate a synthetic network   model estimation approach maximum likelihood estimation given real graph g estimate the kronecker initiator graph θ e.g. ۳x۳ which we need to efficiently calculate and maximize over θ model estimation solution naïvely estimating the kronecker initiator takes o n n۲ time n for graph isomorphism metropolis sampling n  big const n۲ for traversing the graph adjacency matrix properties of kronecker product and sparsity e n۲ n۲ e we can estimate the parameters in linear time o e model estimation experiments autonomous systems internet n ۶۵ e ۲۶۵ fitting takes ۲ minutes as graph is undirected and estimated parameters correspond to that degree distribution hop plot log degree log count number of hops log of reachable pairs diameter ۴ model estimation experiments network value scree plot log rank log eigenvalue log rank log ۱st eigenvector completed work overview s۱ dynamics of network evolution s۲ dynamics of processes on networks g۱ patterns densification shrinking diameters cascade shape and size g۲ models forest fire kronecker graphs cascade generation model g۳ predictions estimating kronecker parameters selecting nodes for detecting cascades information cascades cascades are phenomena in which an idea becomes adopted due to influence by others we investigate cascade formation in viral marketing word of mouth blogs cascade propagation graph social network cascades questions what kinds of cascades arise frequently in real life are they like trees stars or something else what is the distribution of cascade sizes exponential tail heavy tailed when is a person going to follow a recommendation cascades in viral marketing senders and followers of recommendations receive discounts on products recommendations are made at time of purchase data ۳ million people ۱۶ million recommendations ۵ k products books dvds videos music ۱۲.psd product recommendation network purchase following a recommendation customer recommending a product customer not buying a recommended product g۱ viral cascade shapes stars no propagation bipartite cores common friends nodes having same friends g۱ viral cascade sizes count how many people are in a single cascade we observe a heavy tailed distribution which can not be explained by a simple branching process very few large cascades books log cascade size log count does receiving more recommendations increase the likelihood of buying books dvds cascades in the blogosphere posts are time stamped we can identify cascades – graphs induced by a time ordered propagation of information extracted cascades b۱ b۲ b۴ b۳ a b d a blogosphere blogs posts post network links among posts c b d c e e e a b d c g۱ blog cascade shapes cascade shapes ordered by frequency cascades are mainly stars interesting relation between the cascade frequency and structure g۱ …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: network, cascade, graph, model, kronecker, node, dynamics, e, log, g۱, n, property,

این فایل پاورپوینت شامل 47 اسلاید و به زبان انگلیسی و حجم آن 3.91 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/06/15 11:30:13 استخراج شده است.

http://www.cs.cmu.edu/~jure/thesis/jure-proposal.ppt

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