 Minden képe kérdése is képesztek. Egyenek a TEMPRA Szentralitáció pedig kérdése is kérdettek, amit a sárhányi helyzetet képesztek a hengaríteni akadémiával. Olyan a helyzetet is kérdettek a helyzetet, hogy a végzőknek is. Nézzétek, hogy a zenei és a zenei támánkhoz számomra is számomra. A nétváros támánkhoz számomra is számomra, amit a nézéti támánkhoz visszatérünk, és a zenei támánkhoz számomra is számomra. A zenei támánkhoz számomra is számomra, az édeséggel az első kvárt, amik a két találisztok elég előre. A tetséget az elértegyők, és ezért az első kvárt, is van számításra a tetségot, amik a történeteléban van a történetelébe, és ez az már az esélyet. Kifér egy találisztetelében. A zeneval számításra, és az kapcsolatokat. a helyzet kérem, amit a támunkat mondja a témállal, és egyetlen együtt különségeknek és a meglehetőségeket. A munkát is a témállal pedig az, hogy a témállal és a félrentét a témállal kérem is lesz, mondja, hogy a témállal, hogy a témállal, és hogy a témállal, hogy a témállal, hogy a témállal, miatt lehet, hogy ezt lényeg. A témány az idő az éjszakítása, aki van személye és ez az éjszakítás, amit a témányi éjszakítása, az éjszakításra, és a témányi éjszakítás, a témányi éjszakításra, ezek a témányi éjszakítás, olyan tűzegégben érdeljét, azonán mindenképpen főződésnek az őket is. Egy kívánc bennél azt nem olyan, de a pillanatot és a finomuában hozzá bennél, hogy talajdághoz ki a csináltal, aki és a konceptet, ami nem bennél hozzá, Azon az őket, hogy talajdák, ráadja az őket, az őket, és az őket, és az őket, a háromhagyát, , ők az őket, és őket, és őket, és őket, és őket, és őket, és őket, és őket, és őket, Éve krémányos miatt, és a mény boojásből isről van egy m KY2-ben. Úgy lehet, hogy a ZDT-ben röjjünk, és a ZDT-ben kíromtartam. A röjkből megtílomk egy idénig, hogy egyre egy teljesen túlhelyező látoltak, és egy teljesen túlhelyező látoltak, a Twitter-ban megtílomk egy teljesen túlhelyező látoltak. Szóval egy gyertőből. Übbségeimtnek ezeket az embereket is pirulva és kapcsolatokot incsutációt érkezni, ami egyik az érkezők helyetet vannak az ember képviselően, de és igen az érkezős helyetet tetszettek, és az érkezők helyetek tességtől szudni az autóban is. A következő újra lesz, hogy ezt a katonán csapatban és a mákező celtelésből is tűjt különkünk. Ő a gyövös, de az aggóról a kezét kívülésképpet. És a celtelésből az újra egy kockát celtelés. A német, ez kockával az, hogy ez a másik is. Cct. le akartanchesen elisztetem a celtelésből a kockával a kockával, A zene, de a felszítenek nem is означasztunk, de az eszélyt az egész, hogy az egy kis felszítenek is szükséges, és a felszítenek ebben az összínűgésére lehet, de az új állítható, de ehhez egy a helyére is tudja hagyni a szükséges, hogy a színűgésre lehet egy új útat a szíteneket, ez az új felszítenek a szinténkét. Azt mondom, hogy a szítenek is szükséges, Mindenként, hogy most kérdem, hogy ezt a játékot kérdettél, és az, hogy összekevered egy édesképpel, hogy a gyöngyét kérdett, és az a csevét, két édesképpel, és az a két édesképpel, és a szinten, hogy a két édesképpel, hogy a gyöngyét kérdét kérdett, És pedig az edzőség után az er aggállásunk hál elkezdve az erőséget az első főzésre a legnagyobb. A tondául electronikált állatokként egy két évek két két semmit egyedVERTEN tyúját. 1. A-t kényleg a B-t az a kényleg a C-t, a C-t az a küllével. a törző édeskére, ami az egész és a törző édeskére. És a törző édeskére van túl a másik lényegi két kezededőre. A törző egyébként az nem, és a törző egyébként az nem a törző. A finom-két-két édeskére, az egyébként a két kezededőre. A törző egyébként a d-ére, és a finom egyébként a e-ére. A temporozsokat a két centrelét nem van, de csak a számot a számra, a két számra, a két számra, a két számra tett, a számra tett, a két számra tett. A számra, a számra, a számra tett, a számra tett, a számra tett, és ez a számra, amikor meg tudom, hogy megálltok. És az egymásába az éjstart, amit a játásban visszatérjük a helyzetet, és az egykét átárásban drága az eur поступi város miért van. Az a személyére, hogy a személyek semmenni kapcsánokat competingkékek, akkor a másképpen a másképpen a személyére van. A átárála van, hogy szükséges, hogy a máskét önölted az okéval, , mert forduljuk el egy étel, argon a kapcsolat manufacturek´ gép élete, hogy a men fountain'' tetsz rújiból. A Sparadal персонális 형 May λ´s !! A definít組 finns azonsáorg. A continually´s, mert a substációs... egy és a férére, elyen egyetpor Suddenly a dolgozt, és a férére is, azaz az életem, és a férére nagyon erős, ez az az életem, az az a férére is, az az a férére, az a férére, végre az örekezés� állot, és a férére, az örekezés, az örekek az örekek, az örekek, az örekek, a példát és a tennedikába a szegeigben. A tennedtékező rá – a tennedi szérvi határt – és a tennedtékező rá jött a tennedi szeregi. A tennedtékező ránkot erődve az egyik a tennedtékezőről, az nagyon kívánc, ez az emeléról. Lehet együtt kivált vely furcsa. Tudod, hogy a tennedtékezőről együtt kivált velyről van együtt a hely. Nem beült az embereket, motivation and so on. And based on this delays, we assign a date for every edge pair, and then we take the product of these weights, which will define at the end, like the weight of the final path configuration. And in temporal cut centrality, for a given timestamp t, and for a given U, we summarize these weights over all the time respective master and a szükségségben az utolási működére menni. A szerepárom felknálásak a fIB thralland Franklye, szívernemem ilyen szükségségben szerető rendelen, amelyre olyan is kérdéseket élsz meg. Az mindenink mindenekinek jó, aki faljon el tudod, hogy volt egy kis 1-re érdekes FIFA-t. A helyzeteket az együttetése, és a 2-re is egyébként egy akkodnáztok. Köszi a számomra a komzikáció. A számomra nekem nem kérdünk a déléete, úgy kiszteljük a nézhető egyesztel, ami kicsit a változását készíthetek. A finom változáset a kéneagyosat, a zváltozás, hogy legyen szívesen. F infantry say Z, we will be beat out the power z. So just like in cut centrality if we penalize the weight of the waltons the longer the way, the long longer the waltons the less likely it will happen. And you can like, think about this weights as a something like a probability of the waltons a longer walk you see in your data set, the less likely that it was an actual flow information, there are actual flow of goods there in the previous examples. The second waiting that we will use is exponential decay, where besides penalizing the length with beta that we had done before, we are also going to penalize the delays over the edges. And we will saw here the delay will be just inside of an exponential function with some kind of with some predefined constant. As we know that for the exponential function we have this nice property, if we take the product of weights along a specific walk, then at the end we have a very nice formula. So think about again this walk here at the bottom of the slide and say that for every weight defined for the pairs of edges, we have an exponential function defined above, which is based on the delay between the two edges. And if we take the product of these, then at the end from this product we'll just have a simple term here, which is nothing but a penalization as before for the constant weight, which will penalize the length of the walk and there will be another term, which will simply penalize the length of the walk in terms of the time it took the information to flow from the beginning to the end of this time respective walk. So as a summary and as a summary of my notations, temporal concentration is nothing but the sum of all temporal paths that flow to a single specific node u up to some point in time. This sum is always weighted, we can do this weighting by defining a product of weights over the adjacent edge pairs in this time respective walk, specifically what we will take care about the most is when we apply exponential weighting over this walk, in that case specifically we will have a very nice formula for the weight of a specific walk in our sum. Ok, so I defined temporal cut centrality before, but it still remains unclear why I call this centrality metric temporal cut centrality. In the upcoming slides, I will show the relation of our centrality metric to the original static cut centrality. As you are all familiar with right now, cut centrality is nothing but a weighted sum of all paths of length of k, is a weighted sum of all paths ending at a single node u, and here the penalization is again based on the length of the walk. And what I will show you is that for a specific setting, our temporal cut centrality will converge to a very similar formula to the static cut centrality. We will have two assumptions during these calculations, ok? In the upcoming calculations, I will assume that we have a fixed underlying graph, and in this fixed underlying graph we will have an edge set of size e, and what we will do is just we will uniformly sample over time from this edge set, which is defined as an underlying static graph. So we're not going to have any concept in the data, or whatever our time series from which we get the edges will be fully uniform. This will just, it's just going to be a uniform sample with size t from the edges. Our goal now is that given this setting at time t, the question what will be the value of temporal cuts centrality, ok? And we will compute this for both decaying functions. Remember I had two decaying functions, one was the constant beta, and the other was the exponential decay, ok? First we're going to see what happens if we apply a constant weighting along the time respective paths, and then second I will show you what happens in this setting with temporal cut centrality if we have an exponential decay over the time respective paths. Now if we have a constant, then what is like easy to see is that what will be the expected number of times that in this uniform sample a specific path will happen, and this will be t over k times e to the minus k for a specific path with length k, because you have a time series which has length t, from this you want to find in this a path which has length k, and you want to select k examples from these time series, and a specific configuration for a specific configuration of a time respective path, the probability will be e to the minus k. So the expected number for a specific time respective walk in a uniform edge stream will be t over k times e to the minus k. If we know this and if, so based on this, then we can compute temporal cut centrality in a limit when we have a time series of length t, because temporal cut centrality for a specific node will be nothing but the sum of all possible walks ending in that node u, and for a single walk the expected number that we see that walk will be t over k times e to the minus k, and because of constant weighting we'll also penalize a walk with length k with b to the power k, so at the end the expected value of temporal cut centrality over this edge stream if t is large will be the term on the right side which is pretty similar to static cat centrality. Now for exponential decay, the derivation is a bit harder, and here what I want, I will like highlight for you from the derivation. A few tricks first, let's say that STK will be now an expected total weight of length k, after this point we don't know what is the value of this expected total weight for a specific walk, but if we can somehow compute this expected total weight in a net stream for a specific walk then temporal cut centrality will be nothing but the sum of different walks the sum of walks with different lengths and then those multiplied by these weights, and to compute this weight over time there just one single like trick that we should realize which stands specifically for exponential decay and it is if you have a walk that if you have a walk with length k starting specifically here at time t minus j then no matter and ends at time t no matter how the internal edges in the path will appear between t minus j but t the overall weight of a time respective path will be always the same and it will be beta to the k with a exponential decay, right? Now if by assuming this the derivation of the expected value of temporal cut centrality and exponential decay will be simple and I'm not getting into the details but finally what we will have for the expected weights for a specific path in a net stream where the length of the stream tends to infinity will be this very nice formula here at the end and in this formula c is nothing but the decaying term in the exponential function now assume that this c is much smaller than the number of edges from which we sample our time series and say that c is equal to c prime divided by e so this is just like a notation by introducing a new constant instead of our original one by this we can easily like recompute our previous formula and at the end for a exponential decay what we get is that the temporal cut centrality of the nodes in case of a stationary edge stream which tends to infinity will be nothing but a static cut centrality and here in this equation the constant with which we penalize the length of the works will be slightly different so here we have beta divided by c prime remember c prime was the penalization in the exponential function ok, so this is like this was like the theoretical part temporal cut centrality and how temporal cut is related to static cut centrality and what I want to talk about now is that how we can compute actually temporal cut centrality from a graph stream and show you that the computation is very easy and the computation over an edge stream is really based on a simple online update formula to understand this you just have to understand a very simple fact about time-respecting works which is the following say that a new edge appears now in our stream which will point from node v to node u in that case the question how the number of time-respecting works ending yet node u will change a new edge appears so of course a new work with length 1 will appear that will point to you this will be the edge itself and besides that all the time-respecting works that were ending in v will continue to node u and we will propagate all this time-respecting works to node u that originally ended in v hence if a new edge appears in our edge stream then the total increase in centrality for this single node u will be the following formula where the constant one term corresponds to the newly appeared edge and our corresponds to the temporal centrality of node v at that specific time when this edge appeared so like all the time-respecting works that were ending in v at that particular time when this edge appeared will continue to node u and then if time flows and if the current time will be larger than tvu then this formula will be just decayed will be also decayed with our decaying function for temporal cut centrality over this temporal network and it is the following for a single node u at time t we will do nothing but just summarize all edges that appeared before time t and for every edge we will check out the centrality the node that u is connected to at the time when that edge appeared and we will compute a weighted sum over these time-respecting works now here what you should observe and what is interesting is that this centrality here the centrality of node v at the time when the edge was created does not depend on the actual time so this is a fixed weight value and this is always the centrality when that edge appeared in the past so it's kind of like fixed and as time flows by this will not change in the definition and we will heavily rely on this in the actual computation of temporal cut centrality so say that you have an edge stream an edge stream over time and now here I like explain you how finally we can compute temporal cut centrality based on that time series we will initialize for every node u the centrality equal to zero and then we will maintain three quantities the first will be the actual centrality of the node the second will be weights defined over the edges and the third will be the time stamps when the edges appeared over time and what we are doing is whenever a new edge uv appears first we will compute the current centrality score of this node at that given time and then we will propagate this centrality like I explained before to node u and what we will do is that after computing the current centrality score of node v we will save this weight that I was explaining before and which is not changing over time then over this edge so we will define an edge we will define a weight over all all the appearing edges and this weight will be nothing but just one plus the centrality score of this node when that specific edge appeared in time when you have an exponential decay then the formulas will further simplify because of the property of the exponential function the update rules will be just very much simpler the take away message of this slide is that in the previous case we had to compute here in general the centrality score of this node as a summation over its in edges and this step is not necessary and can be done immediately without any summarization if you have an exponential decay function so here the update will be nothing but just the following for every node we will also store when it was the last time updated and the updated centrality score of node v at time t will be nothing but the centrality score the last time we observed multiplied by an exponential decay and if you accept this then the update formula for the centrality score of u a specific node appears the left term is the update of all the old walks that were ending in u in a similar fashion as for v and then for this formula we will add the new incoming edges from the direction of node v to node u before explaining our experiment about a single result which is I think the only centrality metric defined up to this point over time respective walks in the current literature and it's called temporal page rank and this is from Paulina Rosenstern and Aris Dionis actually their work is pretty close to the formulas and definitions I explained you before in their case for temporal page rank the weighting function is somewhat different for them as for us remember for us the weighting function was either a constant term or an exponential decay over time and here in case of temporal page rank the penalization of the walk will be the following say that these two edges appear after each other over time now for an edge pair like this for temporal page rank the penalization will be based on the number of other edges that appeared and started from B after edge AB appeared but before edge BC appeared thinking about the flow of information after this edge appeared then the flow may continue over the edges or already continued over the edges that appeared before edge BC besides defining temporal and centrality of course we try to evaluate the performance of temporal and centrality whether we can use this or how we can use that over edge time series and in general centrality metrics are super hard to evaluate overall because you rarely can conduct supervised experiments to evaluate these metrics for temporal networks the evaluation is even more challenging because if you have a temporal network and that you have a definition of a temporal centrality which evolves over time then the best would be if you could have data sets where you can conduct a supervised evaluation with labels that change and evolve over time now these kind of data sets are very super rare and hard to find we crawled our own crawl over various tennis tournaments so what we did is that we crawled twitter and how people mention each other over a sports event this defined for us a temporal network and besides that we defined labels temporarily changing labels over this network in a way that we checked out for every day of the tournament which people that have actual twitter accounts in twitter are playing for that given day these were defined as relevant samples in our experiments I just want to show you a single result then it's pretty confusing at first but let me explain what's going on here for every day what we try to understand is how the performance in terms of predicting the players that are actually playing right now on that specific day of the tournament how well can these different centrality metrics predict the players over the hours of a given day of course in early hours the twitter mentioned network is inactive because of that methods hardly can predict what's going to happen today and which players are going to actually play today but then over time people start to mention the players that are actually playing on that specific day the different metrics and of course can capture this and better and better over time can understand and predict which players are actually participating in the tournament on the y-axis you can see a bit confusing metric and the CG at 50 I'm not going to explain this now for you a ranking-based metric, the higher is better if you can see their high values it means that a centrality metric can predict the players that are actually playing on that specific day and if the values are low then it is not capable for that as a summary for temporal centrality this is a centrality metric that we define over edge time series the basic definition of that this is the sum of time respective works ending in a specific node the weighting can be arbitrary we define two different weightings one was a simple constant penalization which is based on the length of the work and the second was an exponential decay where the exponential decay penalized less from the beginning of the work to the end of the work after that theoretically we also prove that by using this kind of weighting functions if we assume that our time series is a simple uniform sample from a specific graph then the temporal centrality definition will actually converge and result into something which is really similar to the original static temporal centrality besides that I also try to highlight for you that if you want to compute over time the values of temporal centrality of the nodes then this computation is super easy and we can define an online updating algorithm for temporal centrality so in my final slides what I want to talk about are heavily like work in progress results and questions so we are very happy if you could like give feedback for these issues and problems that we are thinking about the first is very I think reasonable and trivial so for temporal centrality as we had that the centrality metric is the sum of time respective works ending in a single node which is a very reasonable definition but think about the following case what happens in a Twitter mentioned network if first a user mentions CNN because she has seen something in the news portal that she was interested in and then someone else in her social network sees this mention and mentions then you won so then the order of the events in the graph will be that the first mention happened in the past and mentioned between the two users happened in the present and in that case our temporal cot centrality definition will give the highest centrality metric for the node that appeared in the present because the propagation of centrality will always follow the edges that are ordered over time so it would be more reasonable to give a higher centrality metric for CNN and the question how can we do that now a reasonable definition for this kind of centrality metric would be to say that the centrality of a given node is nothing but all the time respecting pass starting from that node and end in the present the problem with this definition is that you cannot define an updating formula for this definition and you're not going to be able to compute that in an efficient way as temporal cot centrality here we have a single idea how we could overcome this but before that let me explain again like why you cannot do why it's hard to compute all the time respective pass that start from a single node assume that in the present now an edge appears in your edge stream which points from B to U then if you would like to count the number of time respective paths that start up to time T at every node then of course the number of walks that starts from this node the value of the number of walks that starts from this node should be updated when this edge appears similarly for all other nodes that are a long time respective paths that end in this edge the number of walks starting from this node and so on should be updated whenever this kind of edge appears which would be really really which would cost a lot if we would like to do a exact computation for that and here I'm just going to sketch a like an idea how it would be possible to become this challenge and remember that temporal cut centrality generally what we were doing is that to compute the current time respective walks that end in a single node we updated weights over all of these edges and these weights were fixed over time the online computation was like whenever a new edge here appears with some of these weights and then we write here an edge value and then this is not going to be touched and this is not going to change over time these weights were actually the number of time respective paths that were flowing from direction V a structure that we can easily online update and I'm just going to note you here a single statement it's the following and I leave you like the derivation of this if you can online maintain this weighted graph and then over that you define a random walk procedure which is the following you start from a single node you compute you start from a single node at time T first you flip a coin and with probability like this you will stop and then if continuing you will continue the random walk over these edges at the end this random walk which will be result of that process will be a time respective walk sample from all the time respective walks proportional to its probability or to its weight again the random walk that we define over these weights is the following first here we stop with the probability which is related to the centrality of node U and then node V, and then we may take a step and then a step and then if we continue the selection will be proportional to these weights over these edges first I have a probability to stop and then selecting from the neighbors will be proportional to the weights over these edges if you continue this at the end you will stop somewhere and then you will have a walk over this weighted graph and this walk will be a time respective walk and this walk will be sampled based with this random walk approach proportional to its actual weight a time respective walk any way if we can do this and we can sample time respective walks on a way that the sampling probability is proportional to their weights based on that we would be able to approximate the number of or the sum of the walks that start at some node and I'm not sure if I maybe I'll have time for this and finally I want to talk about something related to this kind of computations in terms of the fact that we utilize time respective walks but it's like final part of my presentation has nothing to do with centrality matrix it's about network node embeddings which is like a very popular topic currently in computer science now network node embeddings are popular but actually existed long before 2017 and 18 here the objective is to find the dimensional representation of every node in the network on a way that these vectors somehow capture the similarity of the nodes inside of the graph structure now these kind of embeddings existed before for example for bipartite graphs and in the current literature people are trying to somehow improve these methods and they are a little bit different a little bit different a little bit different a little bit different a little bit different a little source of methods and a large portion of currently proposed method that improves the quality of node embedding over static graphs called random walkbas methods like Node2 booking Let me just highlight here for you, like of the code idea Szóval is is szeretem előbb jobban tűnik egy időfektorizáció alakúbb szerep. Ha szükségezik a tetségezésével, mert a szükségezésével és a matrix-fektorizáció, megnézzük a másik fokhagymát, amit adunk optimálni, és amikor tetségeztek, a fokhagymát, hogyjön a fokhagymát, amit akarjuk. Fokhagymát, és a fokhagymát, A dolgok pillanatban van egy kicsit, de egy állókra főzik a két-párált adatokat, és a két-párált adatokat akarunk főzni a két-párált adatokat. A két-párált adatokat akarunk szállni a két-párált adatokat kéne a két-párált adatokat. If these vectors are close each other, it means that they point to a similar direction, which means that their scalar product is high. This is the last function that we generally minimize, for example, with a stylistic gradient descent method, if we want to learn the model parameters, namely pu and pv over a static graph. And then there are random walk-based methods, which appeared recently in the last, like one or two years. Here, the loss function that people generally use is a log-softmax objective, which is somewhat different than mean squared error, but don't take care of it, okay? So this is not the important part of these methods. The interesting part of these methods is that the loss, in the previous case for graph factorization, was defined over adjacent nodes. But in this case, in current papers, there are random walk-based methods, which define some kind of random walk strategies over the static network. And these will generate note pairs over the static network, and the loss function will be defined based on these pairs that are the beginnings and the ends of random walks, which were sampled from the original network. It's like a second or third order similarity. Two nodes are not only similar if are connected in the network, but two nodes are similar if usually random walks connect these two nodes. And here, I just want to sketch for you our future research direction, and we are really open to any kind of comments and critiques, because based on our experiences with time-respective random walks, our ongoing work is nothing but to define an extreme-based online-updatable model, which is based on time-respective walks and can learn temporally evolving embeddings over extremes for temporally evolving networks. And this is all I wanted to talk about. So first, I was explaining for you temporal cut centrality, which we developed, and it is defined generally over S-Line series. It's always the sum of time-respective walks ending in a specific nodes. It is related to static cut centrality, in case the extreme is nothing but a uniform sample from a static graph. Besides that, for an arbitrary extreme, I give you online-updatable formulas for temporal cut centrality. And besides that, we have two ongoing works, which are highly related to this. One is how to compute and how to generalize temporal centrality metrics and not only take care of walks that end in a single node, but also start from a single node. And finally, we intend to develop temporally changing network embeddings, which are also based on time-respective walks. Thank you so much for your attention. Thank you very much. Questions? It was interesting. I don't know if I understood well, but when we were discussing this ongoing work from reversing the order of this centrality measure, essentially we were showing that highly central nodes are those which will essentially absorb walks, whereas low centrality nodes are those that are going to pass it to other nodes. You mean this example? Go ahead. Yes. If we are very central, then the walk is going to be absorbed. If your centrality is very low, you are going to pass it. Yes, because here the concept of this sampling is to select a walk proportional to its probability. If here I have a node with a high centrality, it means that a lot of paths are flowing through this node. And then if a lot of paths are coming from here to this node, then this random walk, something should select, say uniformly over these walks, and then this will continue and end at one of the endpoints of those walks. This sampling strategy here that I explained is for selecting actually time-respective walks from over this network structure, and this stopping criteria is for that. You said that nodes can appear in time, but cannot disappear. Sztóli, it will disappear. Here, all the weights that are proportional to probabilities of these walks are going to slowly disappear with an exponential decay. Thank you. If a node disappears, in an edge stream, that means that it no longer gets edges in its neighborhood. az edzényok főleg nem elkezd Greyville-t. Meg tudom , hogy egyik egy mártény kettősége szólot edzés jön, akkor ha beszélgetve egyik egyik hónapot ellen. A Csíkrét is keresni az accelerációszor? Mi magad az accelerációszor? Miért tudom, hogy a főválóválószokat is látni, hogy a tempora kutatod a nőtől. Aztán az épp tudom, hogy látni, hogy a főválóválószok és a méldeségek is válok. Jé, definitiváltal. Egyébként, hogy a méldeségeket is váljuk egy képvességek, az eszemegők, ami kell lehet, hogy a kis érdemes kis tervet, a kis szerepeje, a meglehet Fruit-ovatél, mivel szeretnék tésztál, hogy elő lesz a kis tésztája, ami kicsi ténesen, és amikor höljünk a kis tésztája, mindenba kérem a kis tészülőkig, a másik zöldökérem, de igen, hogy vallod. De ha szeretnél a kapaportban óvatos, hogy ebben a finom után megint szálljunk. A vannak a személyén azokul endedéreitzéséges. A félzondások, és a szállall decentralítások, mindenki van,庇ftél jobb, de a félzondások és a félzondások tegélszudett. De kifogadalmányhoz, nyitul quindi. Egyébként elölt ségéme, mert nem félített, ami az első félzondás, ezt elvállták a szintetikeket és a félzondások, de a hemmétesen, olyan nructuredeket, és azért egyik� egy bügyé Magalában gondoljuk, hogy a helyikat gondoljuk egy szintén, és a szintén is hangjuk a méltű nőtél, és temporális a kicsit érted a földönnek, és aztán nekik, hogy a felszállal tetszik az edényes fejének, azért melyik felszállal különkült, hogy az olfók, hogy a k Sinális volt rá a különkös világ, a műső helye Montány, a nyilván és a centrelitőmátországban érkeződni. Tudod?